Volume 11 - Volume 11
A Dynamic Data Driven and Data Segregation Approach Image Restoration Using Neural Networks
Abstract
Image restoration is the method of restoring an image to its original state by removing noise and
blur. Image disclarity is crucial to maintain in a variety of cases, including photography, where
motion blur is caused by camera shake when taking images, radar imaging, where the impact of
image system reaction is removed, and so on. Image noise is an unwanted signal that appears in an
image from a sensor, such as a power / energy signal, or from the atmosphere, such as rain or
snow. Coding artefacts, resolution limitations, transmission noise, object motion, camera shake, or
a confluence of events could cause image degradation. With the intention of separating HF and LF
objects, image decomposition is used to decompose the distorted image into a pattern layer (High
Frequency Component) and a framework layer (Low Frequency Component).
Paper Details
PaperID: 1718
Author's Name: A. Gnanasekar, S. Selvi, A.S.U. Soundharyaa, A. Malini and K.R. Ramya
Volume: Volume 11
Issues: Volume 11
Keywords: Neural Network, Ipython, Image Restoration, Image Decomposition.
Year: 2021
Month: April
Pages: 843-849