Self-Supervised Poisson Denoising in Biomedical Imaging: Edge-AI Verification of Deep Unrolling Architectures

Abstract

Biomedical imaging systems typically operate under low signal conditions. As a consequence, the acquired measurements are often affected by Poisson noise, which arises from the stochastic nature of photon counting and varies with the underlying signal intensity. Although supervised deep learning approaches have achieved strong performance in image denoising tasks, obtaining clean reference images (ground truth) is rarely feasible in real clinical settings. In this work, we consider the Poisson2Sparse framework, which combines sparse representation theory with a deep algorithm unrolling architecture and enables self-supervised learning directly from noisy observations. While most existing implementations rely on CUDA-based GPU environments, the present study re-implements and evaluates the framework on Apple M4 hardware using the Metal Performance Shaders (MPS) backend. Experiments conducted on the PINCAT dataset show that the proposed implementation attains a PSNR of 31.88dB at a photon intensity level of 20, corresponding to a medium-noise regime and closely aligning with previously reported baseline results. These findings suggest that advanced self-supervised restoration models can be executed reliably on consumer-grade hardware equipped with a unified memory architecture (UMA), making such approaches more accessible for both research and potential clinical deployment.