Review of deep learning: Convolutional Neural Network Algorithm

Abstract

Delves into the advanced field of image processing based on the use of neural networks to automatically and efficiently improve the quality and detail of images. The thesis explains that convolutional neural networks are one of the types of deep neural networks, and they are specially designed to gain knowledge from big data and extract complex features and patterns found in images. The different layers of the network are discussed in detail, as they handle images incrementally and extract various attributes in each layer. The thesis also highlights the ability of CNN to detect, learn, and improve important details found in images through convolutional, filtering, and data aggregation processes. The proposed CNN model for image enhancement was developed and tested on both medical and normal images. The images were enhanced using the proposed model and compared with other models. Different quality metrics were used to evaluate the results. The results showed that the proposed model can significantly improve the quality of images. The thesis also explores the potential applications of CNN in various fields such as medicine, photography, and space imaging. The use of CNN in these fields can lead to improved diagnosis and treatment in medicine, better image quality in photography, and more accurate and detailed images in space imaging.