Analysis of Impact of Image Restoration and Segmentation on Classification Model

A widely studied problem in computer science is the restoration, segmentation, and classification of images, which involves image processing, computer vision, and machine learning techniques. Deep learning has made significant contributions to this field, bringing machine learning closer to artificial intelligence. Deep learning has a broad range of applications, including in surveillance, healthcare, medicine, and sports. Convolutional neural networks (CNN), a combination of artificial neural networks (ANN) and deep learning techniques, have made incredible advancements in deep learning. This survey compares methods for restoring noisy images, such as wiener filter, wavelet method, and wiener filtering with BM3D technique, using Gaussian blurring and noising methods. The survey also examines the RGB colour model and YCbCr colour model for image segmentation. Image classification is studied using CNN, where the survey discusses various parameters of convolutional neural networks, including activation functions and pooling methods.