Review of subjection between ANN topology and it’s efficiency in solving the problem of improvement image resolution

Abstract

The article discusses the influence of ANN topology on its efficiency in solving the problem of increasing the image resolution. An empirical approach is used to establish the fact and determine the nature of the influence. At the beginning of the article, the most commonly used topological techniques for constructing an ANN, which are used to solve the problem of increasing the image resolution, are described. Then, the process of creating an ANN is described based on the above topologies. After that, the learning process of ANN is described. A supervised learning algorithm was used to train the networks, and a set of 7 000 images was used as training data. At the end of the article, an assessment of the efficiency of the trained ANN is carried out, through which the effectiveness of topological solutions is determined. To assess the performance of an artificial neural network, a validation dataset of 100 image is used. Two algorithms are used to assess the quality of enhanced images: SSIM and PSNR. The interpretation of the results obtained is also given.

Restoration and Enhancement of Fuzzy Defect Image Based on Neural Network

“…Using the inverse imaging process of fuzzy defect image, a down sampling numerical model is constructed to minimize the image cost. Kislov et al [19] obtained that when reconstructing the resolution of the blurred defect image [19], the image reconstruction method is used to set the interpolation function among the image pixels. A pixel is randomly selected and the cubic polynomial is used to build the correlation function between the adjacent pixels of the pixel.…”