% Denoise denoisedImgs = predict(autoenc, noisyImgs); Goal: Increase image resolution while preserving details.
% Train net = trainNetwork(imds, pxds, lgraph, options);
% Prepare noisy-clean pairs noisyImgs = imnoise(cleanImgs, 'gaussian', 0, 0.01); % Build autoencoder hiddenSize = 100; autoenc = trainAutoencoder(noisyImgs, hiddenSize, ... 'EncoderTransferFunction', 'satlin', ... 'DecoderTransferFunction', 'purelin', ... 'L2WeightRegularization', 0.001); 'DecoderTransferFunction', 'purelin',
map = gradCAM(net, I, classIdx); imshow(I); hold on; imagesc(map, 'AlphaData', 0.5); Problem: Detect diabetic retinopathy from fundus images. Solution: CNN classifier + heatmap localization.
% Load pre-trained detector (requires Deep Learning Toolbox) detector = yolov2ObjectDetector('tiny-yolov2-coco'); % Read image I = imread('street_scene.jpg'); % Load pre-trained detector (requires Deep Learning Toolbox)
% Detect objects [bboxes, scores, labels] = detect(detector, I);
% Segment new image C = semanticseg(I, net); B = labeloverlay(I, C); imshow(B); Goal: Remove noise from images (medical MRI, low-light photography). % Read image I = imread('street_scene.jpg')
% Achieved 94% sensitivity, 91% specificity MATLAB abstracts away low-level complexity while giving you full control over neural network architectures for image processing. Whether you are removing noise with autoencoders, detecting tumors with U-Net, or classifying satellite imagery with CNNs, the combination of AI and MATLAB's image processing ecosystem is a powerful toolkit.