Field Using Matlab: Artificial Neural Networks Applied For Digital Images With Matlab Code The Applications Of Artificial Intelligence In Image Processing

% 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()];

% Detect objects [bboxes, scores, labels] = detect(detector, I); imdsValidation] = splitEachLabel(imds

% Load pre-trained detector (requires Deep Learning Toolbox) detector = yolov2ObjectDetector('tiny-yolov2-coco'); % Read image I = imread('street_scene.jpg'); % Detect objects [bboxes

% Denoise denoisedImgs = predict(autoenc, noisyImgs); Goal: Increase image resolution while preserving details. labels] = detect(detector

% Load pre-trained VDSR network net = vdsrNetwork; % Low-resolution image lrImage = imresize(highResImage, 0.25); lrImage = imresize(lrImage, size(highResImage));