Ivković Ratko M, Milošević Ivana M, Milivojević Zoran N
Department of Software Engineering, Faculty of Economics and Engineering Management in Novi Sad, Cvecarska 2, 21000 Novi Sad, Serbia.
Department of Audio and Video Technologies, School of Electrical and Computer Engineering, Academy of Technical and Art Applied Studies, Vojvode Stepe 283, 11000 Belgrade, Serbia.
Sensors (Basel). 2025 Jan 2;25(1):210. doi: 10.3390/s25010210.
This paper presents a Regeneration filter for reducing near Salt-and-Pepper (nS&P) noise in images, designed for selective noise removal while simultaneously preserving structural details. Unlike conventional methods, the proposed filter eliminates the need for median or other filters, focusing exclusively on restoring noise-affected pixels through localized contextual analysis in the immediate surroundings. Our approach employs an iterative processing method, where additional iterations do not degrade the image quality achieved after the first filtration, even with high noise densities up to 97% spatial distribution. To ensure the results are measurable and comparable with other methods, the filter's performance was evaluated using standard image quality assessment metrics. Experimental evaluations across various image databases confirm that our filter consistently provides high-quality results. The code is implemented in the R programming language, and both data and code used for the experiments are available in a public repository, allowing for replication and verification of the findings.
本文提出了一种用于减少图像中近椒盐（nS&P）噪声的再生滤波器，旨在进行选择性噪声去除，同时保留结构细节。与传统方法不同，所提出的滤波器无需中值滤波器或其他滤波器，而是专门通过对周围紧邻区域进行局部上下文分析来恢复受噪声影响的像素。我们的方法采用迭代处理方法，即使在高达97%空间分布的高噪声密度情况下，额外的迭代也不会降低首次滤波后所达到的图像质量。为确保结果可测量并能与其他方法进行比较，使用标准图像质量评估指标对滤波器的性能进行了评估。在各种图像数据库上进行的实验评估证实，我们的滤波器始终能提供高质量的结果。代码用R编程语言实现，实验所用的数据和代码都可在公共存储库中获取，以便对研究结果进行复制和验证。
