Paper Info
Title
SRNHARB: A deep light-weight image super resolution network using hybrid activation residual blocks
Abstract
The quality of the images in all image-based applications and specially in computer vision applications is very crucial. Hence, design of a light-weight high-performance image super resolution scheme that enhances the quality of the acquired images is crucial for satisfactory functioning of such applications. Design of most of image super resolution schemes ignore extracting and processing of the negative-valued features of the images. In this paper, a novel light-weight residual block, which efficiently extracts and processes both the positive and negative-valued features, is proposed. This new residual block is capable of producing a richer set of features in order to improve the super resolution performance of the network using a set of such blocks. The network using the new residual blocks is shown to yield a performance superior to those of the existing light-weight super resolution networks using other types of residual blocks.
Year
DOI
Venue
2021
10.1016/j.image.2021.116509
SIGNAL PROCESSING-IMAGE COMMUNICATION
Keywords
DocType
Volume
Image super resolution, Deep learning, Residual learning
Journal
99
ISSN
Citations 
PageRank 
0923-5965
0
0.34
References 
Authors
0
3
Name
Order
Citations
PageRank
Alireza Esmaeilzehi113.73
M. O. Ahmad21157154.87
M. N. S. Swamy374.51