文献类型：期刊文献

英文题名：A Local Consensus Index Scheme for Random-Valued Impulse Noise Detection Systems

作者：Xiao, Xiuchun[1];Xiong, Neal N.[2,3];Lai, Jianhuang[4];Wang, Chang-Dong[4];Sun, Zhenan[5];Yan, Jingwen[6]

机构：[1]Guangdong Ocean Univ, Sch Elect & Informat Engn, Zhanjiang 524025, Peoples R China;[2]Tianjin Univ, Coll Intelligence & Comp, Tianjin 300350, Peoples R China;[3]Northeastern State Univ, Dept Math & Comp Sci, Tahlequah, OK 74464 USA;[4]Sun Yat Sen Univ, Sch Data & Comp Sci, Guangzhou 510006, Peoples R China;[5]Chinese Acad Sci, Ctr Res Intelligent Percept & Comp, Beijing 100190, Peoples R China;[6]Shantou Univ, Coll Engn, Shantou 515063, Peoples R China

年份：2021

卷号：51

期号：6

起止页码：3412

外文期刊名：IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS

收录：SCI-EXPANDED(收录号：WOS:000652103000009)、、EI(收录号：20212210423330)、Scopus(收录号：2-s2.0-85106455269)、WOS

基金：This work was supported in part by the Key Laboratory of Digital Signal and Image Processing of Guangdong Province under Grant 2016GDDSIPL-02, in part by the Doctoral Initiating Project of Guangdong Ocean University under Grant E13428, and in part by the Innovation and Strength Project of Guangdong Ocean University under Grant Q15090 and Grant 230419065.

语种：英文

外文关键词：Image restoration; Noise measurement; Roads; Indexes; Standards; Oceans; Detectors; Image denoising; local consensus index (LCI); noise detection systems; random-valued impulse noise

外文摘要：The issue of impulse noise detection and reduction is a critical problem for image processing application systems. In order to detect impulse noises in corrupted images, a statistic named local consensus index (LCI) is proposed for quantitatively evaluating how noise free a pixel is, and then an impulse noise detection scheme based on LCI is introduced. First, the similarity between arbitrary two pixels in an image is quantified based on both their geometric distance and intensity difference, and the LCI of arbitrary pixel is calculated by summing all the similarity values of pixels in its neighborhood. As a new statistic, the value of LCI indicates the local consensus of the concerned pixel regarding its neighbors and could also tell whether a pixel is noise free or impulsive. Therefore, LCI can be directly used as an efficient indicator of impulse noise. Furthermore, to improve the performance of impulse noise detection, different strategies are applied to the pixels at flat regions and the ones with complex textures, since distributions of LCI value within those regions are totally different. As for impulse noise filtering, a hybrid graph Laplacian regularization (HGLR) method is introduced to restore the intensities of those pixels degraded by impulse noise. We conduct extensive experiments to verify the effectiveness of our impulsive noise detection and reduction method, and the results show that the proposed method outperforms the state-of-the-art techniques in terms of impulse detection and noise removal.