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Matlab Process Images

MathWorksOfficialCreated by MathWorks

  • Skill
  • Tool integration
  • Software development
  • BSD-2-Clause
  • No issues found

From the author

Load this first for any MATLAB task involving images, photos, scans, frames, volumes, or visual data — reading, writing, filtering, enhancing, denoising, sharpening, deblurring, thresholding, segmenting, masking, detecting, counting objects, removing backgrounds, measuring, registering, stitching, aligning, cropping, resizing, or transforming. Covers image I/O (TIFF, PNG, JPEG, RAW, HDR), histograms, edge detection, color, morphology, deep learning for images, quality assessment, and 3-D volumes. Also for image problems (dark, noisy, blurry, low contrast, uneven lighting, hazy, color cast) or data exploration (folder of images). Includes medical imaging I/O (DICOM, NIfTI, NRRD, medicalVolume) and large/blocked images (blockedImage, whole-slide). Skip if the user just wants you to look at an image, with no MATLAB work. Not for Simulink video-block models, C/C++/CUDA code generation or GPU deployment of a network, or point clouds. Hyperspectral and visual inspection have sibling skills.
— the description by MathWorks

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

This skill serves as the primary entry point for MATLAB tasks involving images, scans, frames, and 3-D volumes. It provides core conventions for image data types, coordinate systems, and display practices, while routing users to specific references for tasks such as I/O, filtering, segmentation, and measurement. It ensures consistent use of Image Processing Toolbox functions and helps users navigate common pitfalls in image analysis workflows.

What it does

  • Provides standardized conventions for image data types and coordinate systems.
  • Routes users to specialized references for image I/O, filtering, and transformation tasks.
  • Defines best practices for displaying images using the imageshow function.
  • Offers guidance on handling 3-D volumetric data and large-scale images.
  • Assists with selecting appropriate functions for segmentation and object measurement.
  • Provides troubleshooting patterns for common image processing errors.

When to use it

  • When performing image processing tasks in MATLAB, such as filtering, enhancing, or segmenting.
  • When working with image formats like TIFF, PNG, JPEG, DICOM, or NIfTI.
  • When needing to understand MATLAB-specific coordinate conventions for image data.
  • When determining the correct toolbox function for a specific image analysis goal.
  • When troubleshooting syntax or type-related errors in image processing scripts.

Advantages

  • Ensures consistent coding patterns across different image processing tasks.
  • Reduces errors by providing clear guidance on coordinate and data type conventions.
  • Simplifies the selection of appropriate tools for complex workflows like 3-D volume processing.
  • Promotes the use of modern display functions like imageshow over legacy alternatives.
  • Provides a structured approach to identifying the correct documentation for specific functions.

Usage examples

“How do I read a DICOM file and display it in MATLAB?”
The agent will guide you to use the medical-data-io reference for reading the file and the imageshow function for display, following the core conventions.
“I need to count the number of objects in a binary image.”
The agent will provide the standard analysis pipeline using bwconncomp and regionprops, ensuring the input is correctly formatted as logical.
“How do I perform 3-D filtering on a volume?”
The agent will direct you to the 3-D volume processing reference to select the appropriate 3-D specific function, such as imgaussfilt3.
“Why is my regionprops measurement returning only one object?”
The agent will explain that the input must be a logical array and provide the code to convert your numeric array to logical.

Install

  1. Download the skill as a ZIP:

    https://autogineer.com/agent-skills/matlab/matlab-process-images/download
  2. Unzip it into the cross-client skills folder of your project:

    mkdir -p .agents/skills && unzip matlab-process-images.zip -d .agents/skills

Documentation by MathWorks

Image Processing

Process, analyze, and transform images using MATLAB Image Processing Toolbox (IPT). This skill routes you to the correct reference for your task and provides core conventions that apply across all IPT work.

When to Use

Loaded for general-purpose IPT work: file and batch I/O, filtering, restoration, morphology, segmentation, measurement, geometric transforms, color, texture, quality metrics, deep learning on images, and image apps. Non-medical microscopy — cell and particle counting on plain IPT — is in scope.

Hand off (and co-load) instead of answering alone: see the handoff table under ## Routing.

When NOT to Use

  • Training a network with no image-specific preprocessing — use Deep Learning Toolbox directly

  • Simulink or Stateflow video blocks — use Simulink directly

  • C/C++ code generation, MEX, GPU acceleration and deployment — use MATLAB Coder / GPU Coder directly

  • Point clouds, 3-D reconstruction, and video stabilization — use Computer Vision Toolbox directly

  • General MATLAB questions unrelated to images

  • Looking at an image yourself (e.g., "look at this screenshot") when no MATLAB work is requested — use your own vision instead

Core Conventions

Image Data Types
TypeRangeWhen to use
uint80–255Display, file I/O, memory-efficient storage
uint160–65535High dynamic range sensors, medical. Camera RAW is n-bit in a uint16 container — rescale with rawinfo BlackLevel/WhiteLevel, not 65535
single / double0.0–1.0Arithmetic, filtering, computation
logical0 or 1Binary masks, segmentation results

Most IPT functions accept integer types directly — do not convert to floating-point unless required.

Default Coordinate Conventions
  • MATLAB images are (row, col) — row is Y (down), column is X (right)

  • size(img) returns [height, width, channels]

  • Spatial functions use (x, y) order for coordinates (e.g., imref2d, regionprops centroids)

  • Pixel centers are at integer intrinsic coordinates; in default world referencing, pixel (1,1) center is at world (1.0, 1.0) and the image edge starts at 0.5

  • imref2d and imref3d can specify non-default spatial referencing to place images into world coordinate systems. By default they use the unit-spaced, integer-pixel-centered coordinate system described here, e.g. Rout = imref2d(imageSize). Functions that do not accept spatial referencing objects assume this intrinsic coordinate system for any operation requiring spatial referencing.

Multichannel Images
  • Grayscale: M×N or M×N×1

  • RGB: M×N×3

  • With alpha: read using [img, ~, alpha] = imread(...) (alpha is the third output)

  • Convert: rgb2gray, im2gray (preferred, handles more input types)

Display Functions — use imageshow, not imshow

Always use imageshow instead of imshow in all generated code — scripts, apps, examples, and quick previews. The signatures are compatible for common cases: replace imshow(...) with imageshow(...). imageshow (R2024b+) handles sub-bit-depth containers (e.g., 12-bit in uint16) via DisplayRangeMode, supports OverlayData for label/mask overlays, and works with viewer2d. imshow lacks all of these capabilities and should not appear in new code.

Title convention: imageshow creates a viewer2d, not an axes — gca does not return it. To add a title, get the viewer from the Image object's parent:

obj = imageshow(im);
viewer = obj.Parent;
title(viewer, "Title Text");

Do not use title("text") or title(gca, "text") after imageshow — these silently fail or create a stray axes title.

Code Execution

Execute code blocks via evaluate_matlab_code before presenting them to the user. If it errors, fix and re-run. This catches type mismatches, wrong argument orders, and nonexistent functions before the user sees them. If execution requires user data or toolboxes not available in the current session, present the code with a note that it should be run with the user's own data — do not loop trying to synthesize stand-in data.

Error Recovery

If code throws an error after execution, load the matlab-read-documentation skill and look up the function's doc page to verify syntax, required arguments, and supported input types before retrying.

When to Ask the User

Ask for clarification before proceeding when:

  • The image type/format is ambiguous and affects the processing approach

  • Multiple valid approaches exist with different tradeoffs (speed vs quality, lossy vs lossless)

  • The task requires choosing parameters that depend on domain knowledge (e.g., object size thresholds, noise characteristics)

  • The user's MATLAB release is unknown and the recommended function may not be available

Routing

Match your task in the tables below. Check the handoff table first. If the task belongs to another skill, load that skill in addition to this one — the Core Conventions above (data types, coordinate order, code execution) apply to all image data. Do not re-derive them from the sibling. If a row in both tables fits, the handoff table wins on workflow; this skill still owns conventions.

  • Unfamiliar data? Load references/image-understanding.md first — but only when class, range, or content is unknown.

  • Starting from a function name, not a task? The function column is illustrative, not exhaustive. To locate any function: grep -rln '<functionName>' references/. To look up syntax details, load matlab-read-documentation.

  • Pipeline tasks: load several references; cap at three unless the task genuinely spans more.

  • Follow the patterns in loaded references. References contain specific code patterns and workflows — use them instead of improvising equivalent code from general knowledge.

  • No row matches? Name the owning product, state the closest IPT fallback, and say so — do not guess a reference.

Check first: is this another skill's job?
If the task involves...Also load
Segmenting anatomy — tumor, lesion, organ, vessel — in CT, MRI, PET, ultrasound; MedSAM; radiomicsNo skill covers this — Medical Imaging Toolbox (segmentMedicalImage, groundTruthMedical); look up the doc page
Displaying or viewing a 2-D image: imshow, imageshow, viewer2d, montage, mask overlay, annotation, colorbar, colormap, figure exportmatlab-display-image
Displaying a 3-D volume: volshow, viewer3d, isosurface rendering, slice viewer, orthogonal viewsmatlab-display-volume
Hyperspectral or multispectral cubes, spectral indices, unmixing, ENVI, band selectionmatlab-analyze-spectral-images
Calling a PyTorch or ONNX vision model from MATLABmatlab-integrate-pytorch-vision
OCR, text recognition, reading text out of an imagematlab-recognize-text (requires Computer Vision Toolbox)
Factory-floor visual inspection: anomaly detection, defect/scratch/dent detection, gauging, pass/fail countingmatlab-use-visual-inspection (requires Visual Inspection Toolbox, R2026b+)
Barcode or QR reading, camera calibrationNo skill covers this — Computer Vision Toolbox (readBarcode, estimateCameraParameters); look up the doc page
Optical system design: lens import, prescription-side MTF, spot diagrams, ray tracing, tolerance analysismatlab-model-optics
In scope: pick the reference
If the task or the user's words involve...Illustrative functionsLoad
inspect, understand, characterize, explore data, look at my images, what kind of image, what am I working with, what's in this folder, describe the data, summarize the dataset, what format, bit depth, class and range, indexed image, alpha channel, metadata, file info, dataset characterization, before I start processingimfinfo, imhist, countEachLabelreferences/image-understanding.md
read, write, load, save, import, export, open an image, convert format, batch, folder of images, TIFF, PNG, JPEG, RAW, HDR, test patternimread, imwrite, imageDatastore, hdrreadreferences/image-io.md
video, footage, frames, read video, write video, VideoReader, VideoWriter, frame rate, video codec, MP4, AVIVideoReader, VideoWriter, readFrame, hasFrame, writeVideoreferences/video-io.md
smooth, blur an image, sharpen, denoise, noise, grainy, filter, kernel, padding, edge-preserving, bilateral, non-local means, frequency filterimgaussfilt, medfilt2, imsharpen, imbilatfilt, imnlmfilt, imdiffusefilt, imfilter, ordfilt2, locallapfilt, wiener2references/filtering.md
contrast, brighten, darken, too dark, washed out, enhance, improve, histogram, equalize, CLAHE, normalize, rescale, blend, arithmetic, type convert, flat-field, illumination correction, vignetting, uneven lightingimadjust, stretchlim, adapthisteq, histeq, rescale, mat2gray, im2single, imadd, imlincomb, imblend, imflatfieldreferences/preprocessing.md
erode, dilate, open, close, structuring element, 2-D morphology, fill holes, remove small objects, skeletonize, thin, tidy up a binary resultstrel, imerode, imdilate, imopen, imclose, imfill, bwareaopen, bwskel, imtophat, imreconstructreferences/morphological-ops.md
deblur, blurry, unfocused, out of focus, motion blur, camera shake, restore a blurred image, deconvolution, PSF estimationdeconvblind, deconvlucy, deconvwnr, deconvregreferences/deblurring.md
resize, rotate, crop, warp, scale, flip, straighten, affine, projective, perspective, undistort, lens distortion, spatial referencing, world coordinates, voxel coordinates, pixel coordinates, coordinate conversionimresize, imrotate, imcrop, imwarp, imtranslate, affinetform2d, imref2dreferences/geometric-transforms.md
align two images, stitch, mosaic, panorama, overlapping shots, seam, motion correction, multi-modal alignment, control points, non-rigid, deformable, homography (feature-based requires Computer Vision Toolbox)imregister, imregtform, imregdemons, imregcorr, detectSIFTFeatures, matchFeatures, estgeotform2d (last three: Computer Vision Toolbox), imregdeform (Medical Imaging Toolbox)references/registration.md
segment or threshold with no user input: binarize, Otsu, watershed, k-means or colour clustering, imsegsam automatic mode, deep-learning segmentationimbinarize, graythresh, multithresh, adaptthresh, watershed, imsegkmeans, imsegkmeans3, imsegisodata, imsegsamreferences/segmentation-automated.md
segment with user input: clicks, points, boxes, scribbles, seeds, prompted SAM, grabcut, active contours, region growing, lazy snapping, superpixels, App Designer segmentation UIssegmentAnythingModel, grabcut, activecontour, lazysnapping, imsegfmm, grayconnected, superpixelsreferences/segmentation-interactive.md
measure, count objects, how many, area, perimeter, centroid, size, shape, width, thickness, gap, diameter, Feret, orientation, detect edges, circles, lines, boundary tracing, ROI, world units, metrology, region, region properties, analysisregionprops, bwconncomp, bwareafilt, bwpropfilt, bwlabel, edge, imfindcircles, hough, bwboundariesreferences/analysis-measurement.md
quality, PSNR, SSIM, sharpness, noise level, is it blurry, compare two images, difference, before/after, MTF (camera/chart SFR), test chart, artifact (register first — full-reference metrics need pixel correspondence)psnr, ssim, multissim, immse, brisque, niqe, piqe, measureSharpness, esfrChartreferences/quality-assessment.md
texture, repetitive pattern, frequency domain, Fourier, FFT, DCT, Radon, Gabor, co-occurrence, GLCMgraycomatrix, graycoprops, entropyfilt, fft2, dct2, radon, gaborreferences/texture-transforms.md
color space, LAB, HSV, hue, saturation, white balance, color cast, color correction, color balance, ICC profile, grayscale conversionrgb2lab, lab2rgb, rgb2hsv, chromadapt, illumwhite, makecform, deltaE, decorrstretchreferences/color-processing.md
3-D array, volume, volumetric, voxel, slice stack, z-stack — filtering, 3-D morphology, 3-D transforms, or measurement applied to a volumeimgaussfilt3, medfilt3, imresize3, regionprops3, bwvolumefilt (R2026b), bwpropfilt3 (R2026b)references/3d-volume-processing.md
deep learning, neural network, train, U-Net, semantic segmentation networks, classify images, pretrained model, transfer learning, fine-tune, inference, augment, training dataset (requires DLT, often CVT)unet, deeplabv3plus, trainnet, semanticseg, imageDataAugmenter, augmentedImageDatastorereferences/deep-learning-imaging.md
build your own image app or GUI: sliders, live preview, parameter tuning, ROI widgetsuifigure, uigridlayout, uislider, drawrectanglereferences/app-building.md
DICOM, NIfTI, NRRD, DICOM-RT, patient metadata, anonymizing, medicalVolume, medicalImage, reading/writing medical formats, extractSlice, volume orientation, medical spatial referencingmedicalVolume, medicalImage, dicomreadVolume, dicomCollection, extractSlice, dicomContours, dicomanonreferences/medical-data-io.md
gigapixel, whole-slide, out-of-memory images, blockedImage, tiled workflows, image pyramids, .svs, .ndpi, .czi, TIFF > 10,000 px, parallel image processing, custom adaptersblockedImage, apply, getBlock, getRegion, selectBlockLocations, blockedImageDatastorereferences/large-image-processing.md

Toolbox Detection

When unsure what's available, check installed toolboxes:

hasIPT = ~isempty(ver('images'));
hasCVT = ~isempty(ver('vision'));
hasDLT = ~isempty(ver('nnet'));
hasMIT = ~isempty(ver('medical'));
hasWavelet = ~isempty(ver('wavelet'));

Most functions in this skill require Image Processing Toolbox. The deep-learning-imaging reference also requires Deep Learning Toolbox. Feature-based registration (detectSIFTFeatures, matchFeatures, estgeotform2d) requires Computer Vision Toolbox.

Release-Gated Features

This skill targets R2024b+ but some features require newer releases. References annotate these with the required release. Key ones:

  • R2024b: imageshow, imblend, viewer2d

  • R2026a: uidraw, uipaint, linkviewers

  • R2026b: bwvolumefilt, bwpropfilt3, uiannotate

If targeting an older release, check with isMATLABReleaseOlderThan("R2026a") and use the fallback patterns documented in the references. To check release notes for a specific function, load matlab-read-documentation.


Copyright 2026 The MathWorks, Inc.


Files

references/color-processing.md

Color Processing

Decision guidance and gotchas for color space conversions, white balance, perceptual color difference, and color-based segmentation.

Prerequisite: Input must be RGB truecolor (M×N×3). For indexed images, convert first with ind2rgb(X, map). For grayscale images, color operations do not apply.

Often loaded with: filtering.md (per-channel processing), preprocessing.md (contrast/normalization), segmentation-automated.md (color-based object extraction).

Choosing a Color Space

SpaceFunction pairWhen to use
L*a*b*rgb2lab / lab2rgbPerceptual operations: color difference, segmentation by color, contrast on L channel only
HSVrgb2hsv / hsv2rgbHue-based segmentation, color selection by "what color is it"
YCbCrrgb2ycbcr / ycbcr2rgbVideo processing, skin detection
XYZrgb2xyz / xyz2rgbIntermediate for white point adaptation
Linear RGBrgb2lin / lin2rgbPhysical light operations (blending, compositing, lighting math)

Rule: Default to converting out of RGB for any operation that interprets color or brightness. RGB is adequate only for per-pixel arithmetic (scaling, masking with a known binary mask) and I/O. For contrast enhancement, segmentation, thresholding, color comparison, blending, or any task where "how it looks" matters — convert to the appropriate space first.

L*a*b* Gotchas

rgb2lab Output Type Matches Float Input

rgb2lab returns floating-point output — double for integer or double input, single for single input:

lab = rgb2lab(uint8Image);    % Output is double
lab = rgb2lab(doubleImage);   % Output is double
lab = rgb2lab(singleImage);   % Output is single

L channel range: [0, 100]. a and b channels: approximately [-128, 128] in theory; narrower for sRGB inputs (roughly -110 to +100).

lab2rgb Can Produce Out-of-Gamut Values

Critical: lab2rgb can return values outside [0, 1] for saturated colors:

rgb = lab2rgb(labImage);
% WRONG: assume [0,1] and save directly
% CORRECT: clip before saving
rgb = max(0, min(1, lab2rgb(labImage)));

This happens when L*a*b* values represent colors outside the sRGB gamut.

Non-sRGB Input

For Adobe RGB or ProPhoto RGB images, specify the input color space: rgb2lab(img, ColorSpace="adobe-rgb-1998"). Use WhitePoint to override the default D65 reference white.

rgb2lab Input Type Trap

rgb2lab interprets double input as [0, 1] range. If your double image has 0–255 values, the result is silently wrong:

% WRONG: double image with 0-255 range — rgb2lab assumes [0,1]
lab = rgb2lab(doubleImage255);

% CORRECT: ensure proper range
lab = rgb2lab(im2uint8(doubleImage255));  % or
lab = rgb2lab(doubleImage255 / 255);

HSV — Hue Wraps Around

Gotcha: Red straddles H=0 and H=1. A simple range check H > 0.9 misses half the reds:

hsv = rgb2hsv(img);
H = hsv(:,:,1);
S = hsv(:,:,2);
V = hsv(:,:,3);

% Red: must check BOTH ends of hue circle
red_mask = (H < 0.05 | H > 0.95) & S > 0.4 & V > 0.3;

% Green: continuous range, no wrapping issue
green_mask = (H > 0.2 & H < 0.45) & S > 0.3 & V > 0.2;

Always include saturation and value thresholds — low-saturation pixels have unstable hue.

White Balance

Workflow: Estimate Illuminant → Adapt
img = im2single(imread("photo.png"));

% Step 1: Estimate scene illuminant (choose one method)
illum = illumwhite(img);   % White patch assumption (brightest pixel is white)
illum = illumgray(img);    % Gray world assumption (average is gray)
illum = illumpca(img);     % PCA-based (most robust for complex scenes)

% Step 2: Apply chromatic adaptation (default "srgb" handles gamma correctly)
balanced = chromadapt(img, illum);
chromadapt Parameters
balanced = chromadapt(img, illuminant, ...
    "ColorSpace", "linear-rgb", ...  % "srgb" (default), "linear-rgb", "adobe-rgb-1998", "prophoto-rgb"
    "Method", "bradford");            % "bradford" (default), "vonkries", "simple"

The illuminant argument is the estimated scene illuminant (3-element RGB vector). The function adapts from this illuminant to D65 (standard daylight).

Gotcha: chromadapt output can exceed [0, 1] — clip if saving to file.

Perceptual Color Difference

deltaE — CIE76 (Simple Euclidean in L*a*b*)
dE = deltaE(img1, img2);                    % RGB input (converts internally)
dE = deltaE(lab1, lab2, isInputLab=true);   % L*a*b* input (skips conversion)

Returns a 2-D map of per-pixel color differences.

imcolordiff — CIE94 / CIEDE2000 (More Perceptually Uniform)
dE = imcolordiff(img1, img2);                              % CIE94 (default)
dE = imcolordiff(img1, img2, Standard="CIEDE2000");        % CIEDE2000 (best)
dE = imcolordiff(lab1, lab2, isInputLab=true);             % L*a*b* input

Which to use:

  • deltaE (CIE76): simple, fast, adequate for large differences

  • imcolordiff with CIE94: better perceptual uniformity

  • imcolordiff with CIEDE2000: best perceptual accuracy, use for quality assessment

Interpretation (CIE76 scale): ΔE < 1 = imperceptible; 1–2 = barely perceptible; 2–10 = noticeable; > 10 = very different. For CIEDE2000, thresholds are lower (ΔE > 5 is already very different).

Color-Based Segmentation

Use HSV when the user names a color category (e.g., "red", "blue"). Use L*a*b* distance when they provide a specific RGB value or sample pixel.

L*a*b* Distance (Best for Specific Target Color)
lab = rgb2lab(img);
target_lab = rgb2lab(reshape(uint8([200 50 50]), [1 1 3]));  % Target color

% Distance in a*b* plane (ignore luminance for illumination robustness)
a_diff = lab(:,:,2) - target_lab(1,1,2);
b_diff = lab(:,:,3) - target_lab(1,1,3);
color_dist = sqrt(a_diff.^2 + b_diff.^2);
mask = color_dist < 30;  % Threshold in perceptual units
HSV Hue Segmentation (Best for "What Color" Questions)

See HSV section above. Good when you want "all red objects" regardless of shade.

CLAHE on Color Images

Apply contrast enhancement to luminance only — never to color channels. Shortcut: rgb2lightness(img) extracts the L* channel directly without a full L*a*b* conversion.

lab = rgb2lab(img);
L = lab(:,:,1);
L_enhanced = adapthisteq(L / 100) * 100;  % Normalize to [0,1] for adapthisteq, scale back
lab(:,:,1) = L_enhanced;
result = lab2rgb(lab);
result = max(0, min(1, result));  % Clip out-of-gamut

Gamma and Linearization

Operations that model physical light (blending, compositing, lighting) must be done in linear RGB, not sRGB:

linear = rgb2lin(srgbImage);     % Remove gamma curve
% ... physical operations (blending, alpha compositing) ...
srgb = lin2rgb(linearResult);    % Re-apply gamma for display

Gotcha: Averaging two sRGB images directly (without linearization) produces incorrect mid-tones. Linearize first for physically correct blending.

Decorrelation Stretching

Enhances color differences for visualization (useful for multispectral or low-contrast color images):

enhanced = decorrstretch(img);              % Basic stretch
enhanced = decorrstretch(img, "Tol", 0.01); % Clip 1% tails to reduce outlier influence

ICC Profiles — When to Use

For standard sRGB work, use direct conversion functions (rgb2lab, rgb2xyz, etc.). ICC profiles are needed only for:

  • Non-sRGB color spaces (Adobe RGB, ProPhoto RGB)

  • Printer/display profiling

  • Color management workflows

profile = iccread("custom_profile.icc");
% makecform/applycform is the correct API for ICC profile transforms.
% Do NOT use makecform/applycform for standard sRGB conversions — use
% rgb2lab, rgb2hsv, etc. directly (simpler, no profile needed).
cform = makecform("icc", profile, profile2);  % Between two profiles
result = applycform(img, cform);

Common Mistakes

MistakeWhy it's wrongCorrect approach
Color math in RGBRGB distances aren't perceptualConvert to L*a*b* for perceptual operations
lab2rgb without clippingCan produce values < 0 or > 1max(0, min(1, lab2rgb(...)))
HSV red check: H > 0.9 onlyRed wraps around: H≈0 and H≈1(H < 0.05 | H > 0.95)
CLAHE on RGB channels separatelyDistorts colorApply to L channel in L*a*b* only
Blending/compositing in sRGBNon-linear gamma causes dark halosLinearize first with rgb2lin
chromadapt output saved without clippingCan exceed [0,1]Clip before imwrite
Using makecform/applycform for standard sRGB conversionsUnnecessary when both ends are sRGB — makecform/applycform are correct for ICC profile workflows (see above)Use rgb2lab, rgb2hsv, etc. directly for sRGB
rgb2lab on double image with 0–255 rangergb2lab assumes double input is [0, 1] — values >1 produce wrong L*a*b*Convert to uint8 first, or divide by 255

Copyright 2026 The MathWorks, Inc.


Requirements and compatibility

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Attribution and licence

Created by
MathWorks
Publisher
MathWorks (Official)
Source
matlab/matlab-agentic-toolkit · skills-catalog/image-processing-and-computer-vision/matlab-process-images · commit 2a7f215
Licence
BSD 2-Clause License (redistribution allowed)
  • LICENSE (added by AutoGineer from LICENSE.md)
  • ATTRIBUTION.md (written by AutoGineer)
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