Validity of non-local mean filter and novel denoising method

作     者：Xiangyuan LIU Zhongke WU Xingce WANG 

作者机构：School of Artificial IntelligenceBeijing Normal UniversityBeijing 100875China 

出 版 物：《Virtual Reality & Intelligent Hardware》 (虚拟现实与智能硬件（中英文）)

年 卷 期：2023年第5卷第4期

页      面：338-350页

核心收录：

学科分类：08[工学] 0714[理学-统计学（可授理学、经济学学位）] 0701[理学-数学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：Gaussian noise Non-local means filter Unbiasedness Effectiveness 

摘      要：Background Image denoising is an important topic in the digital image processing *** study theoretically investigates the validity of the classical nonlocal mean filter(NLM)for removing Gaussian noise from a novel statistical *** By considering the restored image as an estimator of the clear image from a statistical perspective,we gradually analyze the unbiasedness and effectiveness of the restored value obtained by the NLM ***,we propose an improved NLM algorithm called the clustering-based NLM filter that is derived from the conditions obtained through the theoretical *** proposed filter attempts to restore an ideal value using the approximately constant intensities obtained by the image clustering *** this study,we adopt a mixed probability model on a prefiltered image to generate an estimator of the ideal clustered *** The experiment yields improved peak signal-to-noise ratio values and visual results upon the removal of Gaussian *** However,the considerable practical performance of our filter demonstrates that our method is theoretically acceptable as it can effectively estimate ideal images.