Iterative non-local means filter for salt and pepper noise removal |
| |
| Affiliation: | 1. Xidian University, Xi’an, Shaanxi 710071, PR China;2. Science and Technology on Optical Radiation Laboratory, Beijing 100854, PR China;1. School of Electronic Engineering, Xidian University, China;2. School of Computer Engineering, Nanyang Technological University, Singapore;3. School of Information Technology, Jiangxi University of Finance and Economics, Nanchang, China;4. School of Information and Electrical Engineering, China University of Mining and Technology, Xuzhou, China;1. Department of Mathematics and Physics, North China Electric Power University, China;2. School of Science, Communication University of China, China;1. Department of Electronic Engineering, City University of Hong Kong, Hong Kong Special Administrative Region;2. Department of Information Systems, City University of Hong Kong, Hong Kong Special Administrative Region;3. Department of Computer Science, Chu Hai College of Higher Education, Hong Kong Special Administrative Region;1. Dept. of Bioinformatics and Medical Engineering, Asia University, Taichung, Taiwan;2. Dept. of Medical Research, China Medical University Hospital, China Medical University, Taichung, Taiwan;3. Dept. of Electrical Engineering, National Sun Yat-sen University, Kaohsiung, Taiwan;4. Institute of Information Science, Academia Sinica, Taipei, Taiwan |
| |
| Abstract: | Salt and Pepper noise (S&P noise) removal is an active research area in digital image processing. Existing techniques commonly use the local statistics within a neighborhood to estimate the centered noisy pixel, and tend to damage image details due to the image local diversity singularity and non-stationarity. To address this problem, in this paper, iterative nonlocal means filter (INLM) is proposed to exploit the image non-local similarity feature in the S&P noise removal procedure. Moreover, the proposed iterative framework update the similarity weights and the estimated values for higher accuracy. The experimental results show that the proposed INLM produces better results than state-of-art methods over a wide range of scenes both subjectively and objectively, and it is robust to the detection results. |
| |
| Keywords: | Salt and pepper noise removal Non-local means Iterative filtering |
| 本文献已被 ScienceDirect 等数据库收录！ |
|
