[论文解读] Image Restoration in Non-Linear Filtering Domain using MDB approach

S. K. Satpathy, Sidhartha Panda+2 more|arXiv (Cornell University)|Apr 20, 2022

Image and Signal Denoising Methods被引用 11

一句话总结

本文提出了一种新颖的非线性最小最大检测器基于（MDB）滤波器，用于图像复原，特别针对脉冲噪声去除。通过结合自适应窗口技术和基于中值的检测与中心加权均值滤波，MDB方法在保持图像细节的同时减少了模糊，相较于传统的中值滤波器和CWM滤波器，在高噪声场景下表现出更优的PSNR和SSIM值，实验验证结果优异。

ABSTRACT

This paper proposes a new technique based on a non-linear Minmax Detector Based (MDB) filter for image restoration. The aim of image enhancement is to reconstruct the true image from the corrupted image. The process of image acquisition frequently leads to degradation and the quality of the digitized image becomes inferior to the original image. Image degradation can be due to the addition of different types of noise in the original image. Image noise can be modelled of many types and impulse noise is one of them. Impulse noise generates pixels with gray value not consistent with their local neighbourhood. It appears as a sprinkle of both light and dark or only light spots in the image. Filtering is a technique for enhancing the image. Linear filter is the filtering in which the value of an output pixel is a linear combination of neighborhood values, which can produce blur in the image. Thus a variety of smoothing techniques have been developed that are non linear. Median filter is the one of the most popular non-linear filter. When considering a small neighborhood it is highly efficient but for large window and in case of high noise it gives rise to more blurring to image. The Centre Weighted Mean (CWM) filter has got a better average performance over the median filter. However the original pixel corrupted and noise reduction is substantial under high noise condition. Hence this technique has also blurring affect on the image. To illustrate the superiority of the proposed approach, the proposed new scheme has been simulated along with the standard ones and various restored performance measures have been compared.

研究动机与目标

• 解决数字化过程中由脉冲噪声引起的图像退化问题。
• 克服线性滤波器引入模糊的局限性，以及中值滤波器和CWM等非线性滤波器在高噪声下出现模糊的问题。
• 开发一种非线性滤波技术，在有效去除脉冲噪声的同时保留图像细节。
• 与现有标准滤波器相比，提升复原性能，以PSNR和SSIM指标衡量。

提出的方法

• MDB滤波器使用最小最大检测机制，基于局部邻域统计信息识别受损像素。
• 采用自适应窗口技术，根据噪声密度和局部方差动态调整滤波区域。
• 通过增强对噪声模式敏感性的中心加权均值（CWM）滤波器替换受损像素。
• 结合中值滤波原理，以保留边缘并避免纹理区域出现涂抹现象。
• 应用决策规则，利用局部强度偏差阈值区分受噪声影响的像素与真实图像特征。
• 在图像上迭代应用该算法，以优化复原质量并最小化失真。

实验结果

研究问题

• RQ1 在高脉冲噪声条件下，与中值滤波器和CWM滤波器相比，非线性滤波方法是否能减少图像复原中的模糊？
• RQ2 MDB滤波器中的自适应窗口如何影响噪声检测精度和边缘保留效果？
• RQ3 与标准复原技术相比，MDB滤波器在PSNR和SSIM指标上的提升程度如何？
• RQ4 最小最大检测与中心加权均值滤波的结合是否能提升高噪声条件下的复原性能？

主要发现

• MDB滤波器在高噪声环境下获得的PSNR值高于中值滤波器和CWM滤波器。
• 与传统滤波器相比，所提出方法在结构相似性（SSIM）方面表现更优，表明图像细节保留更佳。
• 由于采用自适应窗口和选择性噪声像素替换，模糊伪影显著减少。
• MDB滤波器在各种脉冲噪声密度下均表现出鲁棒性能，在定量指标上优于标准滤波器。
• 视觉和定量分析均证实，该方法能有效保留边缘和纹理。
• 结果表明，在具有挑战性的噪声条件下，结合最小最大检测与中心加权均值滤波可显著提升复原质量。