A total variation model based on edge adaptive guiding function for remote sensing image de-noising |
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| Institution: | 1. Department of Computer and Information Technology, Liaoning Normal University, Dalian City, Liaoning Province 116029, China;2. Department of Mathematics, Liaoning Normal University, Dalian City, Liaoning Province 116029, China;3. Department of Mathematics, Tonghua Teachers College, Tonghua City, Jilin Province 134002, China;4. Department of Computer Science and Technology, Soochow University, Suzhou City, Jiangsu Province 215006, China;1. UMI 233 IRD-UM Inserm U1175 TransVIHMI, Infections Fongique et Parasitaire Laboratoire de Parasitologie et de Mycologie Médicale, UFR Pharmacie, Montpellier cedex 5, France;2. Laboratoire de Parasitologie et de Mycologie - CeDReS (Centre de Diagnostic et de Recherche sur le SIDA et les Autres Maladies Infectieuses), UFR Pharmacie, CHU de Treichville, Université Félix Houphouët Boigny, Abidjan, Ivory Coast;3. Centre de Diagnostic et de Recherche sur le SIDA et les Autres Maladies Infectieuses, CHU de Treichville, Abidjan, Ivory Coast;4. UMI 233 IRD-UM Inserm U1175 TransVIHMI, Service des Maladies Infectieuses et Tropicales, CHU Gui de Chauliac, Montpellier, France;1. Software Engineering Department, Faculty of Engineering-Architecture, Beykent University, Sar?yer, 34398 ?stanbul, Turkey;2. Mathematics Engineering Department, Faculty of Arts and Sciences, ?stanbul Technical University, Maslak, 34469 ?stanbul, Turkey;1. INM – Leibniz Institute for New Materials, Campus D2 2, 66123 Saarbrücken, Germany;2. Department of Materials Science and Engineering, Saarland University, Campus, 66123 Saarbrücken, Germany;1. College of Information Science & Technology, Agriculture University of HeBei, Baoding, HeHei, China;2. Department of Biological Systems Engineering, Washington State University, Pullman, WA, USA;1. Department of Applied Mathematics and Computer Science, Technical University of Denmark, Denmark;2. Department of Wind Energy, Technical University of Denmark, Denmark |
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| Abstract: | The unexpected noise generated during the process of remote sensing images formation and transmission process is a main factor undermining the images’ quality and usage. In recent years, thanks to its local self-adapting characteristics, formal normalization, and modeling flexibility, PDE has received wide attention for its image de-noising functions, thus pushing the realization of maintaining image details while successfully de-noising a new goal for remote sensing images filtering. Having firstly analyzed and discussed the TV model and M model, a modified variation-model (S model for short) based on edge adaptive guiding function is proposed in this paper. The model introduces edge adaptive guiding function based on the standard gradient into the non-linear diffusion term and re-constructed approaching term, which adaptively adjust the smooth intensity around edge and texture information-rich regions of remote sensing images. S-model does not only overcome staircase effect that is easily produced in the TV model, but also avoids losing details and texture information which is often seen in M model, it can efficiently eliminate noises, maintain a good image edge and keep texture details perfectly. The experimental results validate the effectiveness and stability of the proposed model. |
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| Keywords: | Remote sensing image De-noising Total variation Standard gradient Edge adaptive guiding function |
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