Enhanced Total Variation Model Image Fusion using Hybrid Filters and Adaptive Median Method
K.Elaiyaraja1, M. Senthil Kumar2, B. Chidambararajan3
1K. Elaiyaraja, Assistant Professor, SRM Valliammai Engineering College, Chennai (Tamil Nadu), India.
2Dr. M. Senthil Kumar, Associate Professor, SRM Valliammai Engineering College, Chennai (Tamil Nadu), India.
3B. Chidambararajan, Professor Principal, SRM Valliammai Engineering College, Chennai (Tamil Nadu), India.
Manuscript received on 29 May 2019 | Revised Manuscript received on 11 June 2019 | Manuscript Published on 22 June 2019 | PP: 611-616 | Volume-8 Issue-3S, February 2019 | Retrieval Number: C11300283S19/19©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: In order to obtain a thorough and perfect description of the object, need to integrate information from different modalities of the same image. Image restoration causes artifacts and parameter regularizations issues. In the existing Total Variation method, adaptive functional directives are used. These stuffs are based on limited divergence of fused images. These method causes staircase effects. The Enhanced Total Variation (ETV) model is proposed to solve staircase effects and artifacts issues. An Adaptive Median Method (AMM) is proposed to maintain image details and de-noises effectively without creating any staircase effects. Experimental results are produced by using this method for restoration of images. This proposed method has robustness and produce effective result compared to other variation methods for image fusion or restoration.
Keywords: Total Variation, Hybrid Filter, Adaptive Median Method, Image Fusion, Enhanced Total Variation.
Scope of the Article: Image Security