Paper Info
Title
MorphoNet: a Deep Image Super Resolution Network using Hierarchical and Morphological Feature Generating Residual Blocks
Abstract
Morphological operations are nonlinear mathematical operations that are capable of performing signal processing tasks based on the structures and textures of the signals. With this motivation of the capability of morphological operations, in this paper, a novel residual block that can generate morphological features of images and fuse them with the conventional hierarchical features has been proposed. The proposed residual block is then used to design a light-weight deep neural network architecture in a residual framework for the task of image super resolution. It is shown that a fusion of morphological features of images with the conventional hierarchical features can improve the super resolution capability of a deep convolutional network. Experiments are performed to demonstrate the effectiveness of the proposed idea of using morphological operations and the superiority of the network designed based on this idea in super resolving low quality images.
Year
DOI
Venue
2021
10.1109/ISCAS51556.2021.9401522
2021 IEEE INTERNATIONAL SYMPOSIUM ON CIRCUITS AND SYSTEMS (ISCAS)
Keywords
DocType
ISSN
Image Super Resolution, Deep Learning, Morphological Image Processing, Residual Learning
Conference
0271-4302
Citations 
PageRank 
References 
0
0.34
0
Authors
3
Name
Order
Citations
PageRank
Alireza Esmaeilzehi113.73
M. O. Ahmad21157154.87
M. N. Swamy310418.85