IMPROVING IMAGE QUALITY BASED ON THE SRCNN ALGORITHM

Abstract

Abstract. Improving the quality of digital images, in particular, restoring high-resolution versions of low-resolution images, is one of the most relevant research areas in artificial intelligence today. This article considers an image quality improvement algorithm based on the Super-Resolution Convolutional Neural Network (SRCNN) model. SRCNN is the first super-resolution model developed based on a convolutional neural network, which has high efficiency in converting low-resolution images into high-quality ones. The article analyzes the architecture, operating principle, advantages and disadvantages of the model, and highlights areas of practical application. The results show that using the SRCNN model provides much higher quality in restoring image clarity and detail compared to traditional interpolation methods.