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The degradation of the acquired signal by Poisson noise is a common problemfor various imaging applications, such as medical imaging, night vision andmicroscopy. Up to now, many state-of-the-art Poisson denoising techniquesmainly concentrate on achieving utmost performance, with little considerationfor the computation efficiency. Therefore, in this study we aim to propose anefficient Poisson denoising model with both high computational efficiency andrecovery quality. To this end, we exploit the newly-developed trainablenonlinear reaction diffusion model which has proven an extremely fast imagerestoration approach with performance surpassing recent state-of-the-arts. Weretrain the model parameters, including the linear filters and influencefunctions by taking into account the Poisson noise statistics, and end up withan optimized nonlinear diffusion model specialized for Poisson denoising. Thetrained model provides strongly competitive results against state-of-the-artapproaches, meanwhile bearing the properties of simple structure and highefficiency. Furthermore, our proposed model comes along with an additionaladvantage, that the diffusion process is well-suited for parallel computationon GPUs. For images of size $512 \times 512$, our GPU implementation takes lessthan 0.1 seconds to produce state-of-the-art Poisson denoising performance.

Fast and Accurate Poisson Denoising with Optimized Nonlinear Diffusion