IMAGE QUALITY ENHANCEMENT USING NEURAL NETWORKS FOR VARIOUS TYPES OF DISTORTION
The paper considers the problem of enhancing image quality using neural networks with different types of multi-level distortions - contrast degradation, adding noise of various natures, image compression, etc. The widespread TID2013 database, which contains both original images and images modified using various types of distortions (25 basic images, 24 types of distortions, and 5 of their levels), was used as the image database for training neural networks. This database was divided into training (480 images), validation (360), and test (120) images.