An Algorithm for Multi-Fame Image Super-Resolution Under Applicative Noise Based on a Convolutional Neural Network
Sergey Savvin, А. А. Сирота · 2020 2nd International Conference on Control Systems, Mathematical Modeling, Automation and Energy Efficiency (SUMMA) · 2020
The paper considers the problem of multi-frame super-resolution under applicative noise which generates distributed regions of outlying observations (damaged regions) in initial low resolution images. It analyses the existing solutions to the problem based on using recurrent algorithms of optimal filtering of a sequence of low resolution images. The paper also considers the existing super-resolution algorithms based on convolutional neural networks and deep learning models. A new approach and a corresponding image processing scheme is suggested for multi-frame image super-resolution under applicative noise based on using a convolutional neural network. The paper presents the results of the experiment conducted in order to compare the considered approaches to multi-frame image super-resolution by means of a sequence of low resolution frames under applicative noise.