  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.
