2024/01/29 by Dadras, Ali, Leffler, Klara, Yu, Jun
#FOS: Computer and information sciences #FOS: Electrical engineering #Image and Video Processing (eess.IV) #Methodology (stat.ME) #electronic engineering #information engineering
paper · doi:10.48550/arxiv.2401.16099
This paper introduces a novel ridgelet transform-based method for Poisson image denoising. Our work focuses on harnessing the Poisson noise's unique non-additive and signal-dependent properties, distinguishing it from Gaussian noise. The core of our approach is a new thresholding scheme informed by theoretical insights into the ridgelet coefficients of Poisson-distributed images and adaptive thresholding guided by Stein's method. We verify our theoretical model through numerical experiments and demonstrate the potential of ridgelet thresholding across assorted scenarios. Our findings represent a significant step in enhancing the understanding of Poisson noise and offer an effective denoising method for images corrupted with it.