Enhancing Image Quality via Robust Noise Filtering Using Redescending M-Estimators
Arturo Rendón-Castro, Dante Mújica‐Vargas, Antonio Luna-Álvarez, Jean Marie Vianney Kinani · Entropy · 2023
In the field of image processing, noise represents an unwanted component that can occur during signal acquisition, transmission, and storage. In this paper, we introduce an efficient method that incorporates redescending M-estimators within the framework of Wiener estimation. The proposed approach effectively suppresses impulsive, additive, and multiplicative noise across varied densities. Our proposed filter operates on both grayscale and color images; it uses local information obtained from the Wiener filter and robust outlier rejection based on Insha and Hampel's tripartite redescending influence functions. The effectiveness of the proposed method is verified through qualitative and quantitative results, using metrics such as PSNR, MAE, and SSIM.