% Load pre-trained VDSR network net = vdsrNetwork; % Low-resolution image lrImage = imresize(highResImage, 0.25); lrImage = imresize(lrImage, size(highResImage));
% Denoise denoisedImgs = predict(autoenc, noisyImgs); Goal: Increase image resolution while preserving details. % Load pre-trained VDSR network net = vdsrNetwork;
% Train net = trainNetwork(imds, pxds, lgraph, options); % Low-resolution image lrImage = imresize(highResImage
% Detect objects [bboxes, scores, labels] = detect(detector, I); lrImage = imresize(lrImage
% Load and preprocess images imds = imageDatastore('image_folder', 'IncludeSubfolders', true, 'LabelSource', 'foldernames'); [imdsTrain, imdsValidation] = splitEachLabel(imds, 0.7, 'randomized'); % Define CNN architecture layers = [ imageInputLayer([64 64 3]) convolution2dLayer(3, 8, 'Padding', 'same') batchNormalizationLayer() reluLayer() maxPooling2dLayer(2, 'Stride', 2) fullyConnectedLayer(2) softmaxLayer() classificationLayer()];
% Predict pred = classify(net, imdsValidation); accuracy = mean(pred == imdsValidation.Labels); disp(['Accuracy: ', num2str(accuracy)]); Goal: Locate and classify multiple objects within an image.
