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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://mp-staging.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/image-io.md

Image I/O, Conversion & Batch Processing

Gotchas, non-obvious patterns, and decision guidance for image file I/O, type conversion, HDR, video, and batch workflows.

Often loaded with: image-understanding.md (inspect before processing), filtering.md / preprocessing.md (next pipeline step).

Medical Formats

Decision flow:

  1. Check: hasMIT = ~isempty(ver("medical"));

  2. If MIT is available → STOP — use references/medical-data-io.md instead (provides medicalVolume, medicalImage, spatial referencing, RT structures, orientation)

  3. If MIT is NOT available → use the IPT-only patterns below

Quick DICOM Read (IPT Only — use only when MIT is unavailable)

Use dicomread + dicominfo:

% Single DICOM file
info = dicominfo("slice.dcm");
pixels = dicomread(info);

% CRITICAL: dicomread returns RAW stored values — NOT calibrated units.
% For CT, convert to Hounsfield Units:
img_hu = double(pixels) * info.RescaleSlope + info.RescaleIntercept;

For reading an entire DICOM folder as a volume, use dicomreadVolume:

[V, spatial, dim] = dicomreadVolume("path/to/dicom/folder");
V = squeeze(V);  % Always squeeze — removes singleton 4th dim for grayscale
Quick NIfTI Read (IPT Only)
V = niftiread("brain.nii.gz");
info = niftiinfo("brain.nii.gz");

% Key metadata
voxelSize = info.PixelDimensions(1:3);  % [dx dy dz] in mm
imageSize = info.ImageSize;              % [rows cols slices]

% Writing
niftiwrite(V, "output.nii.gz");          % Compressed automatically (R2024a+)
niftiwrite(V, "output.nii");             % Uncompressed
Reminder: Check MIT First

If you skipped the decision flow above: always check ~isempty(ver("medical")) before using the IPT-only patterns. See references/medical-data-io.md for superior APIs (medicalImage, medicalVolume) that auto-apply rescaling and preserve spatial metadata.

Type Awareness — Critical Rule

IPT functions behave differently based on input class. This is the #1 source of silent bugs:

Input classAssumed rangeConsequence
uint8[0, 255]Arithmetic saturates — uint8(200) + uint8(100) = 255, not 300
double/single[0.0, 1.0]Values outside [0,1] clip silently on imwrite
double/single outside [0,1]ArbitraryScientific/computed data — use rescale or mat2gray before display/write
int16[-32768, 32767]Used for some medical/scientific data
logical0 or 1Many format writers reject logical (use PNG, not JPEG)

Most IPT functions accept integer types directly — only convert to floating-point when doing manual pixel arithmetic (e.g., weighted blending, subtraction). See preprocessing.md for detailed guidance on when to convert.

FunctionPurposeWhen to use
im2uint8Float [0,1] → uint8 [0,255]Before imwrite for float data
mat2grayArbitrary range → double [0,1]Non-image data (e.g., FFT magnitude) before saving
ind2rgbIndexed + colormap → RGB doubleAfter [X,map] = imread(...) for indexed images

For floating-point conversion guidance (im2single vs im2double), see preprocessing.md.

JPEG Is Not for Analysis

Never use JPEG for image analysis pipelines. JPEG's lossy compression discards pixel data in 8×8 blocks — this corrupts edges, introduces block artifacts, and alters intensity values. Use PNG or TIFF (lossless) for any intermediate or final images that will be measured, segmented, or compared quantitatively.

JPEG is acceptable only for final display/presentation output where exact pixel values don't matter.

Write Gotchas

MistakeWhy it failsFix
imwrite(doubleImg, "out.png")Values > 1 clip silentlyimwrite(im2uint8(doubleImg), ...)
imwrite(logical, "out.jpg")JPEG rejects binaryUse PNG, or convert to uint8
Writing 16-bit to JPEGJPEG is 8-bit onlyUse TIFF or PNG
Saving analysis images as JPEGLossy compression corrupts measurementsUse PNG or TIFF for analysis
Forgetting "WriteMode","append"Overwrites previous framesSee multi-frame TIFF below

Exif Orientation — AutoOrient

JPEG and TIFF files from cameras/phones often contain an Exif Orientation tag indicating how the image should be rotated/flipped for correct display. By default, imread ignores this tag and returns raw pixel data (which may appear rotated or mirrored).

% Without AutoOrient — image may appear sideways/flipped
img = imread("photo.jpg");

% With AutoOrient — automatically rotates/flips according to Exif tag
img = imread("photo.jpg", AutoOrient=true);

Use AutoOrient=true when reading camera photos or user-supplied images where orientation matters. Check for the tag with imfinfo:

info = imfinfo("photo.jpg");
if isfield(info, 'Orientation')
    fprintf("Exif Orientation tag: %d\n", info.Orientation);
end

HEIC/HEIF Files (R2025a)

iPhone and modern cameras often save as HEIC/HEIF. Read with standard imread:

img = imread("photo.heic");                     % Standard read
img = imread("photo.heic", AutoOrient=true);    % With orientation correction

No special function needed — imread handles HEIC natively since R2025a. Note: WebP is not supported by MATLAB — convert externally before reading.

Camera RAW Files — rawread

Do not use imread for camera RAW files. On some RAW formats (CR2, DNG), imread may appear to work but returns only the embedded 8-bit preview thumbnail — not the full-resolution sensor data. Use rawread or raw2rgb instead:

cfa = rawread("photo.NEF");          % Returns uint16 Bayer CFA data
rgb = demosaic(cfa, "rggb");         % Demosaic to full-color RGB

% Include non-visible frame area (e.g., dark reference columns)
cfa = rawread("photo.DNG", VisibleImageOnly=false);

Key points:

  • Returns raw sensor data (Color Filter Array) — no white balance, no tone curve

  • Output is uint16 or single depending on the file

  • Does not support RAW formats that use JPEG compression

  • Use demosaic to convert CFA pattern to RGB (pattern is camera-specific: "rggb", "bggr", "grbg", "gbrg")

  • For sensors with non-standard bit depth (e.g., 12-bit in 16-bit container), specify BitsPerSample: demosaic(cfa, "rggb", BitsPerSample=12) (R2025a)

Simpler path — raw2rgb: If you just want an RGB image (with white balance and color space applied automatically):

rgb = raw2rgb("photo.NEF");                          % Full processing, uint16 output
rgb = raw2rgb("photo.NEF", BitsPerSample=8);         % uint8 output
rgb = raw2rgb("photo.NEF", ColorSpace="adobe-rgb-1998");

Full manual RAW→sRGB chain (when you need control over each stage):

info = rawinfo("photo.NEF");
cfa  = rawread("photo.NEF");
rgb  = im2single(demosaic(cfa, info.CFALayout));
rgb  = rescale(rgb, InputMin=info.ColorInfo.BlackLevel, ...
               InputMax=info.ColorInfo.WhiteLevel);       % Correct n-bit scaling
rgb  = rgb * info.ColorInfo.CameraTosRGB';                % Camera → sRGB matrix
rgb  = chromadapt(rgb, info.ColorInfo.D65Illuminant, ColorSpace="linear-rgb");
rgb  = lin2rgb(rgb);                                      % Linear → sRGB gamma

Do not skip CameraTosRGB — without it, colors are visibly wrong (ΔE > 2 on every pixel). Use ColorSpace="linear-rgb" with chromadapt because demosaiced RAW data is linear.

Use rawread + demosaic when you need access to the raw CFA data. Use raw2rgb when you just want a usable RGB image.

PNG Alpha Channel

PNG files can contain transparency. The alpha channel is the third output of imread, not embedded in the RGB data:

[img, ~, alpha] = imread("icon.png");  % alpha is [] if no transparency
if ~isempty(alpha)
    % alpha is same size as img(:,:,1), uint8 [0=transparent, 255=opaque]
    composite = im2double(img) .* double(alpha)/255 + background .* (1 - double(alpha)/255);
end

Gotcha: The second output is the colormap (empty for RGB). Do not skip it — [img, alpha] = imread(...) puts the colormap in alpha.

Indexed Images

Some older formats (GIF, 8-bit PNG, BMP with palette) return an indexed image + colormap:

[X, map] = imread("indexed.gif");
% X is uint8 indices, map is Nx3 double colormap [0,1]

% Convert to RGB for processing
rgb = ind2rgb(X, map);  % Returns double [0,1] RGB

Gotcha: If you call imread with one output on an indexed image, you get indices that look like a dark grayscale image — not the actual colors.

Animated GIF Writing

Writing animated GIFs requires indexed frames and a specific append pattern:

for i = 1:numFrames
    % Convert each frame to indexed color (required for GIF)
    [A, map] = rgb2ind(frames{i}, 256);

    if i == 1
        imwrite(A, map, "animation.gif", "gif", ...
            LoopCount=Inf, DelayTime=0.05);
    else
        imwrite(A, map, "animation.gif", "gif", ...
            WriteMode="append", DelayTime=0.05);
    end
end

Key parameters:

  • LoopCount — number of loops (Inf = forever, 0 = play once). Only specify on the first frame's imwrite call.

  • DelayTime — seconds between frames (0.05 = 20 fps)

  • All frames should use the same colormap for best results

Write Options — JPEG & TIFF

% JPEG quality (0–100, default 75)
imwrite(img, "out.jpg", Quality=95);

% TIFF compression
imwrite(img, "out.tif", Compression="lzw");       % Lossless, good general choice
imwrite(img, "out.tif", Compression="packbits");   % Fast, moderate compression
imwrite(img, "out.tif", Compression="none");       % No compression, largest file
FormatParameterValues
JPEGQuality0–100 (default 75). Use 90–95 for archival or when downstream processing needs high fidelity. Use 50–75 for web or storage-constrained use. Below 50 introduces visible artifacts.
TIFFCompression"none", "lzw", "packbits", "deflate"
PNGBitDepth8 or 16

Multi-Frame TIFF

The append pattern is non-obvious — first frame uses default write, subsequent frames must specify "WriteMode","append":

for i = 1:numFrames
    if i == 1
        imwrite(frames{i}, "stack.tif");
    else
        imwrite(frames{i}, "stack.tif", WriteMode="append");
    end
end

Reading specific frames: imread("stack.tif", frameIndex)

Frame count: numel(imfinfo("stack.tif"))

Low-Level TIFF — Tiff Class

For tiles, strips, custom tags, or BigTIFF:

t = Tiff("image.tif", "r");
data = read(t);
nextDirectory(t);    % Move to next frame
close(t);

HDR & EXR Images

High Dynamic Range (HDR) and OpenEXR (EXR) images have floating-point values that can exceed 1.0.

% Radiance .hdr format
hdr = hdrread("scene.hdr");       % Returns M×N×3 float, values can exceed 1.0
hdrwrite(hdr, "output.hdr");

% OpenEXR format (R2022b+) — common in VFX/compositing
[img, alpha] = exrread("scene.exr");  % Float data + optional alpha channel
exrwrite(img, "output.exr");
info = exrinfo("scene.exr");          % Metadata (channels, resolution, etc.)

Tone mapping (HDR → displayable LDR). Input must be single or double:

ldr = tonemap(hdr);                                    % Global → uint8
ldr = localtonemap(hdr, RangeCompression=0.8, ...
    EnhanceContrast=0.5);                              % Local contrast-aware

Create HDR from bracketed exposures:

files = ["under.jpg", "normal.jpg", "over.jpg"];
hdr = makehdr(files, RelativeExposure=[1 4 16]);  % Relative exposure ratios
ldr = localtonemap(hdr);

Video I/O

See references/video-io.md for full video reading/writing guidance including VideoReader, VideoWriter, profiles, frame counting, and common pitfalls.

Batch Processing with Datastores

transform — Process on Read
imds = imageDatastore("data/", IncludeSubfolders=true);

% Apply processing lazily — images are transformed when read, not upfront
tds = transform(imds, @(x) imresize(im2gray(x), [256 256]));
img = read(tds);  % Returns processed 256×256 grayscale
combine — Pair input/label datastores
inputDS = imageDatastore("images/");
labelDS = imageDatastore("masks/");
paired = combine(inputDS, labelDS);
data = read(paired);  % Returns {inputImg, maskImg}
Folder-Based Labels
imds = imageDatastore("data/", ...
    IncludeSubfolders=true, ...
    LabelSource="foldernames");  % Subfolder names become labels

Note: augmentedImageDatastore requires Deep Learning Toolbox — see references/deep-learning-imaging.md.

Partial Reads

For large images, avoid loading the full file:

sub = imread("big.tif", PixelRegion={[1 500], [1 500]});  % Rows 1-500, cols 1-500

For very large images (gigapixel), see references/large-image-processing.md.

Synthetic Test Images

FunctionWhat it createsKey detail
phantom(N)Shepp-Logan phantomReturns double, range includes negative values near zero
checkerboard(tileSize, M, N)Checkerboard patternReturns double [0,1], size is 2*M*tileSize × 2*N*tileSize (a tile is a 2×2 block of squares)
imnoise(img, type, ...)Noisy copyAccepts and returns same class as input

Noise types: "gaussian" (mean, var), "salt & pepper" (density), "speckle" (var), "poisson"

Functions That Do NOT Exist

These are commonly hallucinated — do not generate calls to them:

Hallucinated functionWhat to use instead
estimateNoiseSee noise estimation in image-understanding.md
imreadstackLoop with imread(filename, frameIdx) for multi-frame TIFF
iminfoCorrect name is imfinfo
imread3dUse dicomreadVolume, niftiread, or loop over slices
imwriteallLoop with imwrite(..., WriteMode="append")
rgb2grayscaleUse im2gray
imresize3dCorrect name is imresize3

Common Mistakes

MistakeWhy it's wrongCorrect approach
Arithmetic on uint8Saturates at 0/255, no negativesConvert to single first, or use imadd/imsubtract
Assuming imread returns doubleReturns uint8 by defaultAlways convert or check class(img)
Using rgb2grayErrors on non-RGB inputUse im2gray — handles more input types
imwrite(floatImg, ...) without convertingClips silently outside [0,1]Use im2uint8 or mat2gray first
read(v) on large videoLoads ALL frames into memoryUse hasFrame/readFrame loop
imread("photo.NEF") for camera RAWSilently returns only the embedded 8-bit preview thumbnail, not full-resolution sensor dataUse rawread then demosaic
[img, alpha] = imread("file.png")Second output is colormap, not alpha[img, ~, alpha] = imread(...)
imwrite(rgb, "out.gif")GIF requires indexed color[A,map] = rgb2ind(rgb, 256) then imwrite(A, map, ...)
imwrite(..., "JPEGQuality", 95)Wrong parameter nameUse Quality=95 for JPEG
Saving analysis images as JPEGLossy compression corrupts pixel values for measurementUse PNG or TIFF for any image that will be analyzed
niftiwrite(V, "out.nii.gz", Compressed=true)Verbose since R2024aniftiwrite(V, "out.nii.gz") — auto-detects from extension
imread for EXR filesimread does not support EXRUse exrread (R2022b+)

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