Paper Info
Title
Development Of New Fractal And Non-Fractal Deep Residual Networks For Deblocking Of Jpeg Decompressed Images
Abstract
The JPEG compression scheme introduces blocking artifacts when the images are decompressed. JPEG image deblocking schemes based on deep neural networks map a JPEG decompressed image to its corresponding deblocked image. Employing a residual block that is capable of generating a rich set of high frequency residual features in a deep JPEG image deblocking network can improve its representational capability, and therefore, enhance the network performance. In this paper, we propose two residual blocks that generate rich high frequency residual features. The first residual block generates features from the high frequency component of its input signal in addition to generating conventional hierarchical residual features using convolutional operations. The second one is a fractal residual block that is developed by replacing the conventional convolutions in the first block by the block itself. The two proposed residual blocks are, respectively, used in recursive (non-fractal) and non-recursive (fractal) neural networks for the task of JPEG deblocking. The results of the experiments performed on the two proposed deblocking networks show their performance superiority over the respective state-of-the-art deblocking networks.
Year
DOI
Venue
2020
10.1109/ICIP40778.2020.9191030
2020 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP)
Keywords
DocType
ISSN
Deep Learning, JPEG Image Deblocking
Conference
1522-4880
Citations 
PageRank 
References 
0
0.34
0
Authors
3
Name
Order
Citations
PageRank
Alireza Esmaeilzehi113.73
M. O. Ahmad21157154.87
M. N. S. Swamy374.51