Local Lab Controls
- 1 What kind of local control?
- 2 What are the challenges we need to solve?
- 3 General principles
- 3.1 The RT-spot object
- 3.2 Partitioning into 4 zones – previewing the RT-spot area
- 3.3 The "normal" and "excluding" types of RT-spots
- 3.4 Tint, chroma, luminance references and the algorithm principle in “normal” mode
- 3.5 Why no alternative algorithms?
- 3.6 Functioning in “inverse” mode
- 3.7 Maximum number of RT-spots
- 3.8 The “super” algorithm
- 3.9 Artefact reduction and the “Global quality” algorithms
- 3.10 Avoid color shift
- 3.11 “mip” files – Principles of the multi-point algorithm
- 4 Specificity of the local mode (as compared to “Lab adjustments”)
- 5 A specific use case : reduction of image defects (dirty sensor, red eyes, …)
- 6 Settings in the Preferences dialog
- 7 Processing time and memory use
- 8 Future evolution
1 What kind of local control?
Several types of local control exist in mainstream applications :
- “lasso”, associated with layers and fusion masks (e.g. Photoshop©). This kind of control is widespread (Photoshop, Gimp, Darktable…) and users tend to favor those, at the expense of the type described in 3. below, which is less well known;
- dedicated tools for the removal of image defects such as “red eyes”, or “spots” associated with sensor imperfections or dirt;
- “U-points” controls, used until recently in Nikon Capture NX2©, or as an add-on to other software such as Nik Software©. For those (rare now) who tried such programs, there’s no way turning back to layers and fusion masks! This type of local control requires learning something different, and a cognitive “reprogramming”. In my opinion, this kind of local control is globally more efficient.
The algorithm developed in the current version of RawTherapee is close in its principle to the U-points described above. Of course, the code used in Nik Software is not disclosed, but several years ago I was attracted by the simplicity of use and the efficiency of U-points, and I started developing a product which would use neither lassos, nor layers or fusion masks. Of course, nothing prevents us from having all 3 types of local control in the same program.
The current version of the filter is perfectible, particularly regarding the user interface (GUI):
- for managing and viewing the RT-spots (control spots) on screen,
- for manipulating the different sliders, which would ideally be better if integrated to the actual control points, as done in Nik Software.
However, those two aspects don’t harm everyday use, nor code portability.
In order to improve manipulation and avoid long menus on screen, I added a GUI interface which is similar to the one used in the Wavelet tab, with “expanders”. Thus for each module (“Color and Light”, “Blur”, “Retinex”, …) you can choose to:
- display (or not) the full list of commands: sliders methods, curves…
- activate (or not) all the functions of the module.
Warning: The second choice activates or cancels all the RT-spots. (It would be possible in theory to add this capability to every single RT-spot, but for what purpose?)
1.1 Lab and RGB local controls
- The first algorithms are coded in L*a*b* mode with the following modules: “Color and Light”, “Blur / Add noise”, “Retinex”, “Tone mapping”, “Sharpen”, “Contrast by Detail Levels”, “Denoise”. Two additional modules have been added: “Exposure” and “Vibrance”.
- See below for the principles of the different algorithms, but one key point to remember for shape detection and artifact limitation, is that the Lab mode preserves the “hue” tint, which is crucial.
- The idea of an “RGB” module comes to one’s mind, with a minima:
- a “White balance” module, which would allow to locally adjust the white balance, for example to warm up shadows, or to deal with mixed scene lighting such as “daylight” and “tungsten”. This module needs to be inserted just after “demosaic”.
1.1.1 Auto White balance
The “White balance” module (in the “autowblocal” branch) serves two purposes:
- allowing a local adjustment of the white balance (of course)
- testing new algorithms for automatic white balance adjustment:
- in general use (full image), in order to achieve better results than with the current algorithm. Of course one will tell me that I should create a specific branch for “White balance”, but here I’m trying to hit two targets with one bullet.
As a reminder, I’ve been asked in 2012 and 2013 to develop an iterative white balance “Auto Robust WB”. At that time, another developer and I had spent a lot of time on this for an unsatisfactory result, so we gave up. Since then, benefiting from this experience, I resumed this work but in the reverse way! Now, if I’m correct, the algorithm works. I took advantage of this new capability (currently working on raw images only) to search in the academic literature for other auto WB algorithms. I found, and developed two new algorithms:
- “auto edge”: the idea is to create an accentuation mask allowing to reinforce parts of the image where details are more prevalent, compared to flatter regions.
- “auto standard deviation”: here, the image (or part of it for the local control) is divided in 12 zones, for each of which mean and standard deviation are computed, and the result assembled.
A third algorithm, “Color by correlation”, looks promising but I’m facing several difficulties. After many trials of many types, the results are just too erratic…
I also added the notion of gamma:
- the same principles which deal with luminance distribution – gamma sRGB – used in the main RawTherapee code.
Bear in mind that the automatic white balancing is a totally non deterministic mathematical problem. The algorithms may perform more or less well (and faster or slower), but a perfect result can’t exist! Why is it so? If one is referring to the principles of color management (), it is necessary to have knowledge of the three of: the colored object, the illuminant and the observer. At least two of those are unknown in practice. In addition, the environment in which an image is shot is not known while it has an effect on the perception (CIECAM), and the same is true during both image editing and viewing. The mission is thus almost impossible!
The aim of the “White balance” module is for testing the algorithms, there are 7 of them at least, and twice that number if “gamma sRGB” is considered. Indeed, in order to circumvent the obstacles encountered five years ago, instead of the using the image before demosaicing, I used the image just after demosaicing, allowing to apply (or not) “gamma sRGB”, ending in the same conditions as in normal use (but without normal “White balance” and “ICC or DCP” profile).
Ideally, we should test the algorithms on many images, as well: which algorithm(s) to keep and implement in normal – global RGB – mode (maybe one or two, in addition to the current algorithm which we should keep for compatibility).
1.1.2 Local control of White balance
The local control is currently limited to 1 spot only, but there’s no difficulty to increase this if users want so… while waiting for the evolution of “locallab”.
Of course, I hear voices say “it doesn’t work”, or “the output doesn’t match with the preview”. But it does work! And it is more than evident that matching exactly the preview is by definition almost impossible… (differences between shadows, sun light, different illuminants, etc…) whatever the relevance of the algorithm.
2 What are the challenges we need to solve?
Several general issues need to be solved so as to achieve smoothness in use:
- allow an (almost) unlimited number of RT-spots (the name we chose for the control spots in RawTherapee);
- adapt the local algorithms to be aware of scale, because many of them take the image size – the treated area – into account. This aspect is fundamental, particularly with curves which act on the whole image;
- minimise memory use and computation time for JPG and TIFF output;
- allow easy updates in case of evolution of either the algorithms or the number of methods/modules;
- optimise (minimise) the differences between the preview and the output.
For each RT-spot:
- allow for the action to occur, on user choice, either inside or outside (“Inverse” mode) of the selection area;
- allow shape detection, on user choice, in order to limit the action based on features/shapes;
- take care of the transition between the center of the treated area and the other parts of the image;
Currently, RT-spots are operational in Lab mode with the following modules and methods:
- “Color and Light”: “Lightness”, “Chrominance”, “Contrast” in normal mode with 4 curves L=f(L), L=f(H), C=f(C), H=f(H), and in “inverse” mode;
- “Exposure”: normal mode only;
- “Vibrance:” normal mode only;
- “Blur & noise”: normal and “inverse” modes;
- “Retinex”: normal and “inverse” modes, now also with “transmission gain” curve;
- “Sharpening”: normal and “inverse” modes;
- “Contrast by detail levels” (CBDL): normal mode only;
- “Denoise”: normal mode only.
3 General principles
3.1 The RT-spot object
As I mentioned earlier, the system used in RT is similar to the one developed in Nik Software, but with some significant differences:
- each RT-spot can be viewed as an object with a number of fields (about 70 as of today), made of sliders, curves, control points, etc…;
- each field, grouped in modules, can be activated or not, and have different parameter values according to their nature;
- the modules are made to be coherent for the user: “Color and Light”, “Exposure”, “Contrast”, “Vibrance”, “Blur”, “Retinex”, “Sharpening”, “Tone Mapping”, “Contrast by detail levels”, “Denoise”;
- the number of RT-spot objects can vary from 2 up to 500;
- data describing the RT-spot objects are stored in text files with .mip extension;
- creating, modifying and tracking of the RT-spot objects are done in a “for” loop;
- there’s no code duplication.
3.2 Partitioning into 4 zones – previewing the RT-spot area
When the user selects an RT-spot, the screen preview shows:
- a centre, made of a circle whose diameter and position can be adjusted directly using the mouse or the sliders;
- two horizontal and two vertical handles, whose position can be modified directly using the mouse or the provided sliders, controlling the extent of the area affected by the RT-spot;
- if "Show spot delimiters" is checked in "Preferences" > "General" tab > "Local adjustments", one can visualise approximately the area affected by the spot. Because I couldn’t work cleanly with the « arc/ellipse/scale » function in the Cairo graphics library, I made each quadrant defined by a pseudo-ellipse made of 3 Bézier curves joining each other. In most cases the approximation is satisfying… To help grabbing the 4 handles, I made them longer (the Bézier curves are inactive!).
We obtain 4 zones, whose orientation cannot be modified (the default rotation angle is set to zero). Those 4 zones have each of their vertices connected by imaginary ellipses.
It is possible to drag the handles outside the image preview area.
It should be possible to replace the ellipse with a hand drawn curve (even if I think its usefulness is debatable because of the existence of the shape detection algorithm), as long as the homothetic transform of the curve doesn’t intersect with the original curve. Although the benefits from this intellectually satisfying “improvement” should negligible in the case of the RT-spots, it will be possible to make the current RT-spots and lasso type of controls coexist eventually.
3.3 The "normal" and "excluding" types of RT-spots
Two types of RT-spots are available:
- "Normal": these are the spots which do the local retouching. Each new RT-spot takes into account the effect of existing spots when their zone of influence overlap (a sort of recursive action).
- "Excluding": adding a new "excluding" RT-spot allows to revert the effect created by a "normal" RT-spot in their overlapping zone of influence , from the original image data (but doing its calculations from the reference values of the area affected by the "normal" RT-spot).
For both types, the user has access to most of the modules ("Color and Light", "Contrast by detail levels", "Blur", ...)
3.3.1 Comments about the "excluding" RT-spot
- The principle of the "excluding" spot is similar to the "counterpoint" in Capture NX2, and is useful when for example, the effect of a "normal" RT-spot bleeds into an area which the user wants to be unaffected. This unwanted effect happens when when the tints of the two areas (the area the user wants to be affected, and the area one wants to remain unaffected) are to close.
- The underlying algorithm is similar to the main algorithms used elsewhere in "locallab", and is based on tint differences. While it's not perfect it should give satisfaction 70% of the time.
- I implemented an additional algorithm (with no guarantee that it will actually work one day) to help discriminate the areas, based on a Sobel-Canny transform. The transform is coded but is not yet accessible to the user, as I'm still working to make it more efficient.
3.3.2 How to use the "excluding" RT-spot, and its limitations
- Create a new RT-spot over the area you want to restore.
- In the "Settings" panel, from the "Spot method" combobox choose "Excluding spot".
- Adjust "Scope", "Transition" and "Spot size" as well as the shape of the zone of influence using the 4 handles, until you get the desired reversion of the effect. You can use complimentary adjustments such as "Color and light", etc.
The algorithm computes the reference values (tint, chroma, luma) from the the current image (see below). As a consequence, the "excluding" effect won't work if the image area is black (for example when the user uses a normal RT-spot using "Color and light" in inverse mode to create a black frame). Sometimes, when the "inverse" mode is selected, the "excluding" spot algorithm may not work as expected and lead to a difference between the preview and the output (JPG, TIFF) image. Increasing the value of "Scope" of the "excluding" RT-spot should help in this situation.
3.4 Tint, chroma, luminance references and the algorithm principle in “normal” mode
In order to develop an efficient shape detection algorithm:
- The central circle zone is used as the reference. Based on the diameter value chosen by the user, the algorithm computes the tint (hue), chroma and luminance averages.
- The choice of the central zone diameter depends on the use intent. For example, if working on a foliage area the user would benefit from choosing a smaller diameter value in order to select only the foliage green; in contrast, for skin tones the user should increase the diameter to avoid the detection of parasites (noise, eye lashes…).
- For each quadrant, depending on the the value of the “Scope” slider, the algorithm does:
- take into account the tint difference between the zone centre and the affected pixel;
- through a complex algorithm based on the deltaE notion (perceived difference between two colors, taking tint, chroma and luminance into account), decrease the amount of applied effect as a function of the difference between the central zone and the affected pixel;
- follow either a linear or a parabolic law to adjust the amount of applied effect, based on the specific the case.
This allows adapting the amount of effect according to the above-mentioned criteria. For example, if the central circle is located on foliage, the action will be limited to the leaves, without touching the background (which would be impossible to obtain with the lasso type of controls). Additionally, if some other foliage resides within the area covered by the RT-spot, it will also be affected.
- Adjusting the value of the “Transition” slider modifies the transition of the effect (from the centre to the edge): on 50, the inner (linear) half will get the full 100% action/effect, while the effect in the outer half will gradually transition from 100% to 0% towards the edge;
- By increasing the value of “Scope”, progressively more of the selected area is taken into account, whatever the tint, chroma and luminance;
- Conversely, by decreasing the “Scope” value the effect will get limited to the pixels which are more similar (in terms of deltaE) to the reference zone.
The shape detection algorithm works in “Normal” mode except for some modules (“Denoise”). It’s not implemented in “Inverse” mode, doing so would not make sense.
The algorithm will see its performance increase when the user chooses “Enhanced” or “Enhanced + chroma denoise” in the “Global quality:” combobox. The second choice additionally applies a low amount of chroma noise reduction in order to get rid of artefacts which could be brought up by the “Enhanced” algorithm (note that the chroma denoise step increases memory use and computing time).
The algorithm is used in full capacity for “scope” < 20.
3.5 Why no alternative algorithms?
It should be possible to implement other shape detection algorithms than the deltaE-based one used currently, for example:
- edge detection algorithms such as “Canny” or “Sobel”;
- wavelet decomposition.
But in both cases, everything would have to be done!
3.6 Functioning in “inverse” mode
When it is available, the “Inverse” mode is extremely simplified: only the “Transition” slider can be used to apply the effect. As of late October 2017, I devised a new method for “Inverse” mode. It is limited to the “Blur & Noise” module (see the corresponding section below).
3.7 Maximum number of RT-spots
By default the number of available RT-spots is 8. You can choose any number between 2 and 499. For this you only need to change the parameter “Nspot” in the “options” file (for example: Nspot=12). Restart RawTherapee for the modification to be taken into account.
3.8 The “super” algorithm
The key to “success” was the algorithm implemented in late January 2017, which works this way:
- on the pixel data which are within the area covered by the RT-spot, the algorithm applies either a “traditional” curve, a slider, or a normal transfer function (“Retinex”, “Tone Mapping”, “Contrast by detail levels”, “Vibrance”, …);
- the LUT or the transfer function is converted into an equivalent of a slider, separating negative and positive values so that the function can be viewed as multiple sliders (as many sliders as there are pixels) in the -100, 0, +100 interval;
- the data are passed to the shape detection function – the 4 quadrants. For every pixel, the difference in terms of tint, chroma and luma is computed with regards to the central reference. The linear variation equations are estimated for luminance (L=f(L)), chroma (C=f(C)) or transfer functions;
- different corrections are applied to account for the environment (sky, skin tones…)
- a correction is made (with threshold and iterations) in order to avoid correcting (or only slightly, based on the user’s choice) the grey or neutral tones which are in the same tint as the reference (but with a low chroma value);
- the parameters are weighted based on the shape of the RT-spot (ellipses) and the transition zone;
- the values of the “Global quality:” parameters are important, particularly so for images with a low amount of noise – chroma noise is interpreted by the algorithm being of the same color as the spot. It is thus necessary to suppress this noise, that is the purpose of “Enhanced + chroma denoise” (this algorithm may not be sufficient, though – in this case using the local “Denoise” module may be useful).
This new (“super”) algorithm seems to perform generally well, but in the event when the RT-spot is located in a grey/neutral or flat area, the algorithm may fail. In such cases, using the old algorithm is recommended (except for “Retinex”, “Tone Mapping” and “Contrast by detail levels” for which it is made unavailable for the sake of code simplicity).
3.9 Artefact reduction and the “Global quality” algorithms
By default, “Global quality:” is set to “Enhanced + chroma denoise”. Whereas it increases computation time, it is still generally preferable. In case one wants a more responsive preview, “Enhance” or “Standard” can be chosen (good for exploring the image).
- two sliders allow reducing artefacts and improving the algorithm’s rendering. They work based on chroma, in neutral/gray tones. To completely remove its effect, one can simply set “iterations=0”. These sliders may be used for "Color light", "Exposure", "Retinex", "Vibrance", "Tone mapping" and "Contrast by detail levels";
- for (even slightly) noisy images, the algorithms may be misled. In this case it is recommended to keep "Global quality:” set to “Enhanced + chroma denoise", and (sometimes) activate the local “Denoise” module and adjust the sliders very gently (including “Luminance”);
- under some circumstances, with “Tone Mapping” for example, the artefact reduction algorithm may actually generate more artefacts. When it happens, set “iterations=0”.
If your images are generally clean (no or very low noise), you can choose to have “Standard” as the default for “Global quality:” – this will speed up the image processing. To do so, go to “Preferences” > “Performance and quality” tab > “Local adjustments” and choose the “Standard” algorithm among the 3 options. This will become the new default when new RT-spots are created, but of course the algorithm can still be changed on the fly by choosing from the “Global quality” combo box.
3.10 Avoid color shift
The “Avoid color shift” checkbox serves two purposes:
- put the colors within the current working profile gamut, using a relative colorimetry;
- adjust colors using a “Munsell” correction – particularly for red-orange and blue-purple colors, when saturation in the L*a*b* space has changed significantly.
3.11 “mip” files – Principles of the multi-point algorithm
For the local control system to work and keep track of RT-spot objects, a text file – similar to a pp3 – is written in 2 possible places:
- next to the edited image file. If, for example, you edit an image named ASC4509.NEF, a new file named ASC4509.NEF.mip will be generated in the same folder. In this case, several sessions can be open for the same image, as long as copies of the image are stored in different folders. However, the folder names in the path have to contain ASCII characters only, otherwise it will make the program crash;
- in the dedicated “mip” folder, within the “cache” directory (through an MD5 hash number which identifies the file). This is the default. If chosen, when multiple copies of the same file exist in distinct folders, editing them creates as many “mip” files. This option allows the use of non ASCII characters in the folder names and path.
The choice between these two behaviours is made in “Preferences” > “Image processing” tab > “Mip profiles”.
The “mip” files contain the data which are passed to RawTherapee through processes of the “procparams” type, and LUTs which allow editing in zoom mode. When updating RawTherapee to a new version, it may be required to delete the “mip” files (or the whole cache) to avoid a potential crash of the application. This can be achieved by going to “Preferences” > “File browser” tab, then choosing “Clear mip” and/or “Clear pp3” – all this is valid only if “mip” and “pp3” files have been set to be saved to the cache folder, otherwise they will have to be manually deleted from the working folder. Keeping older “mip” and “pp3” files may lead to instability or crashes of the application.
3.11.1 Architecture of the filter
When I “ventured” into a multi-point, no-layer and no-mask system, code duplication had to be avoided. Indeed, it is unthinkable to consider, for 10 RT-spots, generating 10 GUI code blocks, and just as many code blocks in “rtengine”. After careful consideration, the idea was to have a file updating and tracking in real time – i.e. a file which would follow each modification made by the user – the actions/modifications history.
Of course, the parameters used by RawTherapee for the GUI and for the main code through “params” are of different types:
- numeric: integer, float, double,…
- string (in choice menus for the different modules)
- curves, through “vectors” of type double with dynamic dimension
3.11.2 Data conversion
In order to simplify data management, the first step is to convert all data to integers. Sometimes this can lead to a slight precision loss, which I think is negligible.
- This operation is very simple for numeric values, even though in some cases (such as hue, which is expressed in radian in the interval [-Pi, +Pi]) it needs a double “float*100 and /100 conversion to keep precision.
- It is logical for Boolean operations and methods.
- However, it is a complex matter regarding curves. The values to be recorded in the “mip” file are of type vector<double>, in a dynamic table of numeric values. There may be a simpler way, by using existing functions from “procparams”, but I didn’t succeed.
Nevertheless, I could get around this by:
- converting doubles to integers with 3 significant figures – which is largely enough,
- then converting the “vector” to a variable length character string,
- the writing and reading of this character string and converting to a vector dynamically using an appropriate function (”strcurv_data”). Note that this function allows adjusting the curves within the limit of 16 inflection points (Bézier curve) for the flat curve, and 32 points for the diagonal, which should be enough. Whether this would not be enough, it should be possible to increase that number, though compatibility would be compromised.
3.11.3 Some elements about curves
The implementation of curves has been a complex task, particularly since:
- each curve needs its own reset function – resize() at each iteration,
- which implies oddities regarding their description, for example the diagonal curve gets initialised with several points, despite it looks being “empty”. This is mandatory for correct functioning, but this means that the curve is always open/active. Consequently, to avoid some unwanted loss of computation time in “preview” mode, the user has to activate the curve by clicking on the “curves” combobox. The same activation is necessary for L=f(H) and C=f(C) curves. While clicking enabling the “curves” (linear) would seem to be a better solution, it didn’t work despite many trials.
- during all the procedure, we need to set each "params" value to "clear" for curves, for each LUT, at each iteration and then through a "vector" to reassign the good, updated values to "curve params".
3.11.4 Data storing and dynamic use
Once data have been converted:
- they are stored in a text (mip) file,
- each one is referenced inside a 2-dimension dynamical table:
- dataspot[x][y] for numeric and boolean values and methods, where “x” is the representative index of the parameter (e.g. “9” is for “lightness”) and “y” represents the current RT-spot (control spot). Value “0” represents the current in-use value (the one stores in “params”).
- retistr[y], llstr[y], lhstr[y], ccstr[y], hhstr[y], skinstr[y], pthstr[y], exstr[y] for parameters of type “curve”. Currently there are only 8 parameters, used for local “Retinex”, “Color and light” (L=f(L), C=f(C), L=f(H), H=F(H)), “Vibrance” and “Exposure”, but it is easy to add more. “y” is used as above in 1.
The way data are stored and used fits well with improccoordinator.cc (current management / preview) and simpleprocess.cc (TIFF / JPG output) files, but not so with dcrop.cc and the partial display of the image (RawTherapee’s pipeline).
In this case (zoom / preview) another solution had to be found in order to synchronise the modifications in real time. I made use of LUTs, so that to each entry referenced in the table described above a LUTi(z) is associated, with z=500 possible entries (500 corresponds to the current maximum number of RT-spots, but this limit can be decreased or increased). The LUTi “z” corresponds to the dataspot “y”. A 25,000 entries LUTi (arbitrary number) is used for “retistr”, “llstr”, “lhstr”, “ccstr”, “hhstr”, “skinstr”, “pthstr”, “exstr” for storing each “vector” curve value in memory (up to 69 values stored as integers).
During data processing, exchanges occur between: params, dynamic tables and LUTi. The values used by dcrop.cc are dynamically linked to dynamical tables by parent→.
3.11.5 Exchange processes and dynamic data storing
- reading of the “mip” file to check the version number and guide the updates
- creating the “mip” file if it doesn’t exist
- storing the data in LUTi and dynamic tables from the values stored in “params” (index 0 values)
- first interactive update with the GUI through a transfer function between “rtengine” and locallab.cc (aloListener → localretChanged). This is one of the 3 functions required for the process to work correctly
- reading of the “mip” file and data storing in dynamic tables (index values ==> y) according to the number of RT-spots
- creating a loop – according to the number of RT-spots – which updates LUTi (required by “dcrop”) and “params” values from values stored in the dynamic tables
- call to the “Lab_local” function which contains the algorithms controlling each RT-spot (luminance, sharpness, retinex, denoise, …)
- second interactive update with the GUI through another transfer function between “rtengine” and “locallab.cc”
- treatment of the current RT-spot by using the index (0) values from the dynamic tables. These values are attributed to 1) “params”, 2) LUTi and 3) current index values
- call to the “Lab_local” function for the current RT-spots
- saving to the “mip” file.
Note that if the user modifies the curve (amplitude, number of inflection points) a third interactive update is made in order to conform all of the data according to events.
Of course everything looks simple, the process works if, and only if, all the data are dynamically updated. I’ve tried a lot, went groping, and finally found a working solution… but an uncanny one. There’s probably a more “informatically” correct way to do it, but at this point I’ve done as explained above and hereafter.
The 3 exchanges between “rtengine” and the GUI occur with 3 parameters which actually do nothing in the program except doing the update process:
- “anbspot” which takes values of 0 or 1
- “cTgainshaperab” (curve) which takes any value bewteen 0.70 and 0.90
- “retrab” which varies around 500.
I have added 3 more parameters (in the form of sliders) – hueref, chromaref and lumaref – hidden from the user but which are used to update the system in real time, to correctly take into account the reference “spot” values. Allowing those 3 parameters to vary brings stability and allows real time updates. Increasing the number of those “fantasy” parameters should not be necessary. These extra parameters are hidden to the user, and are only visible in the history.
In addition, I had to add some empirical time delays, giving time to time… Time delay is used for example when the user modifies the curve (amplitude, number of inflection points).
4 Specificity of the local mode (as compared to “Lab adjustments”)
Hereafter is some useful information for the user, often specific to the local mode.
4.1 Color and Light
- The algorithms used for luminance and contrast in local mode differ from the ones used in “Lab adjustments”, which may lead to differences in rendering.
- The luminance algorithm is made such that below -90 and down to -100 for “Lightness”, it is possible to increase the image's “darkness”, almost to the point of getting a luminance value close to 0. To accentuate this effect, “Chrominance” can be decreased and “Scope” increased.
- The inverse mode can be used for creating graduated frames. If “Ligthness” is set to -100 and "Chrominance" is reduced, the “frame border” will be black.
- For the “Lightness” and “Contrast” sliders, the user can choose among 2 algorithms: the first one (default) is close to that in “Lab adjustments” while the second one, activated through “Lightness - Contrast ‘Super’” is close to the algorithm used for the L=f(L) “Super” curve.
- Do not hesitate, when needed, to activate “Enhanced” or “Enhanced + chroma denoise” in the “Global Quality:” combobox.
- An L=f(L) or C=f(C) curves allow controlling the luminance and chrominance for each RT-spot as a function of luminance or chrominance.
- An L=f(H) curve allows controlling the luminance for each RT-spot as a function of tint.
- An H=f(H) curve allows controlling the tint for each RT-spot as a function of tint.
The curves are activated by selecting one of the two algorithms from the “Curves types” combobox:
- “Normal”: the curves – particularly the one that is the most difficult to manage, L=f(L) – use an algorithm close to the one used with the sliders (Normal mode),
- “Super”: using a new algorithm, which I think performs very well (it even surprised me the first time I used it).
Caution: when the RT-spot resides in an area which is neutral, grey or very uniform, the artefacts introduced by the new algorithm (“Super” algorithm for the curve or the slider) may be impossible to get rid of. Use one of the 2 older algorithms to avoid that.
The “Exposure” module may look like the the one in global RGB mode, but:
- it works entirely in L*a*b* mode, hence the differences in rendering;
- there’s no “Lightness”, “Chroma” or “Contrast” slider, whose functions are already fulfilled in the “Color and Light” module;
- there’s only one “Contrast” curve, similar to the L=f(L) firm “Color and Light”. Of course its effect is different from “Tone curve” which works in RGB mode. If needed, two L=f(L) curves can be activated, one under “Color and Light” and the other under “Exposure”.
This module is similar to the main one (from RawTherapee's "Exposure" tab).
4.4 Blur & Noise
“Blur” is activated only when “Radius” =< 2. By significantly lowering the default value of “Scope” and possibly checking “Blur luminance only”, it is possible to get a different amount of blur for the different tints. Since October 2017, 3 methods are proposed:
- “Normal”: the shape detection algorithm has been improved, it is now closer to shape detection algorithm proposed in the other modules;
- “Inverse”: a simplified algorithm, which doesn’t make use of “Scope”. The inversion is coarse and doesn’t allow a fine separation between the affected and unaffected areas;
- “Symmetric”: this new algorithm uses the same processes as “Normal”, and adjusting “Scope” the user can target the process with more precision.
- Using the “Inverse” mode will blur large portions of the image, which can not be corrected later on.
- The action of “Scope” may sometimes appear puzzling: for instance, in a portrait, the eyes (which of course differ from the skin) may be blurred if the value of “Scope” is too low.
4.5 Tone Mapping
Caution when uniformly colored areas (sky, grey/neutral zones) have a similar tint to the area on which the RT-spot is placed. In cases where this leads to artefacts, set the “Iterations” slider to 0 in “Settings” > “Reduce artefacts – Improve algorithm”.
Compared to the standard “Retinex” module, the local “Retinex module has simplified controls, and is applied at the end of the pipeline instead of the beginning. Since “mip” version 10002, the “Transmission gain” is specific to each RT-spot.
Only the “RL Deconvolution” algorithm is provided here.
4.8 Contrast by detail levels
Compared to the main RGB mode module, this one has:
- 5 levels instead of 6,
- no slider for “skin tone” protection (not needed since the algorithm works locally on the RT-spot area),
- an additional “Chroma” slider.
Caution when uniformly colored areas (sky, grey/neutral zones) have a similar tint to the area on which the RT-spot is placed. In cases where this leads to artefacts, set the “Iterations” slider to 0 in “Settings” > “Reduce artefacts – Improve algorithm”.
Compared to the main RGB mode module, this one has:
- only the wavelet tool working (no Fourier function, nor medians),
- less sliders and curves,
- the possibility to work differentially on fine or coarse noise for both luminance and chrominance noise.
5 A specific use case : reduction of image defects (dirty sensor, red eyes, …)
“Locallab” had not been thought with the idea of correcting small image defects. However, some visible, “photographically disturbing” defects may actually be tamed or even removed. Of course, this is not incompatible with other more specialised modules which could be introduced (or not) into “Locallab”… At least 3 existing modules can serve this purpose, alone or in combination, by adapting their use:
- “Color and Light” for spot-like defects;
- “Contrast by detail levels” for spread out defects;
- “Blur and Noise” as an optional complement (caution, this is a destructive action, use moderately).
These modules can be used either on the main image (raw, TIFF, …) or on a flat-field image.
5.1 Using the “Color and Light” module
- Activate “Color and Light”
- A red eye removal tool equivalent can be obtained by framing closely around the eye – central circle zone centered on the eye’s red, spot handles close to the eye – low “Scope” value, then lowering “Lightness” and “Chrominance” to -100.
- The same idea allows reducing the “IR spot” kind of defect by framing closely around the defect – central circle zone centred on the defect, spot handles close to the defect area – then lowering “Chrominance” to taste, and if needed lowering the “scope” value.
- In some (simple) situations, a “dirty sensor” defect can be reduced, in particular in the case of “sensor grease”. It can be seen as stains or spots on a uniform background. To attenuate this, choose a tight framing of around the defect – central circle on the centre of the defect (adapt the size of the RT-spot), drag the handles so that they are not too close to the border of the defect (this will make the transition less noticeable). Then: a) decrease “Transition” towards lower values; b) check “Lightness - Contrast “Super”; c) adjust “Lightness” and “Chrominance” as needed to bring the defect area closer to the unaffected area; d) if needed adjust slightly “Scope” to achieve the best result.
5.2 Using the “Contrast by detail levels” module
When the sensor is dirty (grease), and when the affected area is large or if there are multiple small defects, “Contrast by detail levels” may be used, working as a sort of “wavelet” tool on luminance, and if needed on chrominance. In such cases: a) place the RT-spot on one of the stronger defects (adjust the size as needed); b) drag the handles so as to cover most of the affected area; c) set “Transition” to higher value; d) activate “Contrast by detail levels” and decrease the contrast of levels 3 and 4 (or lower); you can adjust the “Chroma” slider if needed.
6 Settings in the Preferences dialog
Below is a list of the settings (already mentioned above in several places) which can be accessed from the “Preferences” dialog:
- In “Preferences” > “General” tab > “Local adjustments”, by checking the “Show spot delimiters” checkbox, an approximate zone delimitation is drawn which helps viewing the area affected by the RT-spot and its settings.
- If your images generally have low noise, you can set the default for “Global quality” to “Standard”, and get a significant speedup in processing time. This setting is found under “Preferences” > “Performance & Quality” tab > “Local adjustments”. The three choices for quality are selected from the “Local adjustments Quality method” combobox. Default is “Enhanced + chroma denoise”.
- Regarding “mip” files:
- the choice for “mip” files destination folder is set from “Preferences” > “Image processing” tab > “Mip Profiles”;
- in order to clean the generated “mip” profiles, go to “Preferences” > “File Browser” tab > “Cache Options” and click on the “Clear mip” button and/or “Clear pp3” – this is valid in the case where you chose to store “mip” files in the cache, otherwise if you chose to let RT write the “mip” files next to the input file, you will have to delete them manually. Note that the presence of older versions of “mip” and “pp3” files may lead to instability or even crashes of RawTherapee.
7 Processing time and memory use
At image export time (output to JPG, TIFF…), for the “normal” mode, the algorithms compute the data only for in the RT-spot delimited areas, not for the whole image. Thus, processing time and memory stay reasonable. Note that of course, this will depend on the number of RT-spots, their size, and the type(s) of treatment done in these RT-spots.
Excluding “Denoise” and “Sharpen”, processing time is in the order of a few tenths of a second per RT-spot, and memory use of a few hundred MB.
Two settings have a strong influence on resource use:
- “Denoise” which adds around 1 sec. of processing time, and 1 MB of memory use;
- “Global quality: Enhanced + chroma denoise” which adds around 0.5 sec. And 0.6 MB.
These figures depend on the size of the RT-spot.
8 Future evolution
Besides usual tweaking, bug corrections, improvements (settings, user interface)… it would be desirable to improve the GUI with regards to the creation/selection/modification of individual RT-spots.
From a programming point of view, it should be possible to improve code readability and maintainability, and to integrate all the “mip” files code parts (as done in “rgbproc.cc”) which currently are linearly integrated to void procedures in “improccoordinator.cc”, “dcrop.cc” and “simpleprocess.cc”.