Open AccessSuper Resolution Image Reconstruction Using Iterative Regularization Method and Feed –Forward Neural Networks
Author(s) -
M. Ganesh Babu,
Sudam Sekhar Panda,
Homer Benny Bandela
Publication year - 2019
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1228/1/012021
Subject(s) - regularization (linguistics) , artificial neural network , computer science , resolution (logic) , image (mathematics) , iterative method , artificial intelligence , noise (video) , high resolution , superresolution , image resolution , iterative reconstruction , algorithm , remote sensing , geology
Super resolution is one of the best existing procedures to acquire high resolution image due to its effortlessness and extensive variety of use in numerous fields of science and engineering. There are various methods exist for super resolution but, this work made an effort by combining the iterative regularization method with neural network and obtained a good result comparatively from the previous method because of its high compatible nature with less and user friendly approach. It takes care of the noise in the initial stage and gets a concrete result when neural network is introduced. In addition to the noise it controls the vulnerable parameters and gets a highly super resolution image as compare to the literature.