Paper Info
Title
Murnet: A Deep Recursive Network For Super Resolution Of Bicubically Interpolated Images
Abstract
In many real-world cases such as printer devices and in-camera interpolation, only the interpolated versions of the low-resolution images are available. In this paper, a new low-complexity high-performance image super resolution network is proposed that starting from the bicubic interpolated version of the low resolution image produces a high quality super resolved image. The main idea in the proposed scheme is the development of a feature generating block that is capable of producing features using multiple local spatial ranges and multiple resolution levels, fusing them in order to provide a rich set of feature maps, and using them in a recursive framework. The objective in designing such a recursive block is not simply to provide a light-weight network, as is traditionally done in the design of such a network, but also to provide a low count on the number of multiply-accumulate operations with high performance. The experimental results are provided to show that the proposed network outperforms other recursive super resolution networks when their super resolution capability, the number of parameters and number of multiply-accumulate operations are simultaneously taken into consideration.
Year
DOI
Venue
2021
10.1016/j.image.2021.116228
SIGNAL PROCESSING-IMAGE COMMUNICATION
Keywords
DocType
Volume
Image super resolution, Recursive convolutional neural networks, Deep learning
Journal
94
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. Swamy326718.50