Abstract
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.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
