Image enhancement approaches often assume that the noise is signal independent, and approximate the degradation model as zero-mean additive Gaussian noise. However, this assumption does not hold for biomedical imaging systems where sensor-based sources of noise are proportional to signal strengths, and the noise is better represented as a Poisson process. The MICCAI paper explores a sparsity and dictionary learning-based approach and presents a novel self-supervised learning method for single-image denoising where the noise is approximated as a Poisson process, requiring no clean ground-truth data. Experiments on two bio-imaging datasets demonstrate that our method outperforms the state-of-the-art approaches in terms of PSNR and SSIM. Our qualitative results demonstrate that, in addition to higher performance on standard quantitative metrics, we are able to recover much more subtle details than other compared approaches.

Title: Poisson2Sparse: Self-Supervised Poisson Denoising From a Single Image

C.K. Ta*, A. Aich*, A. Gupta*, A. Roy-Chowdhury, International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), 2022 (* joint first authors)