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

Locate and classify multiple objects within an image.

% Denoise a test image testImg = X_train(:, 1); noisyTest = X_noisy(:, 1); denoised = predict(autoenc, noisyTest); Locate and classify multiple objects within an image

% Using pre-trained ResNet-18 net = resnet18; lgraph = layerGraph(net); lgraph = removeLayers(lgraph, 'fc1000', 'prob', 'ClassificationLayer_predictions'); newLayers = [ fullyConnectedLayer(2, 'Name', 'fc_new') softmaxLayer('Name', 'softmax') classificationLayer('Name', 'classout')]; lgraph = addLayers(lgraph, newLayers); lgraph = connectLayers(lgraph, 'pool5', 'fc_new'); In this article, we will explore the applications

% Load pre-trained VDSR network net = vdsrNetwork; In this article

The field of image processing has witnessed significant advancements in recent years, thanks to the integration of artificial intelligence (AI) and machine learning (ML) techniques. One of the most popular AI techniques used in image processing is Artificial Neural Networks (ANNs). In this article, we will explore the applications of ANNs in digital image processing using MATLAB, a popular programming language used extensively in image processing.

% Simulate the neural network output = sim(net, img_dataset.inputs);