MindX: Denoising Mixed Impulse Poisson-Gaussian Noise Using Proximal Algorithms
This addresses image denoising for applications like medical imaging or photography, but appears incremental as it builds on existing transformations and proximal algorithms.
The paper tackled the problem of blind denoising of images corrupted by mixed impulse, Poisson, and Gaussian noises, achieving superior performance compared to state-of-the-art methods on standard images under various noise conditions.
We present a novel algorithm for blind denoising of images corrupted by mixed impulse, Poisson, and Gaussian noises. The algorithm starts by applying the Anscombe variance-stabilizing transformation to convert the Poisson into white Gaussian noise. Then it applies a combinatorial optimization technique to denoise the mixed impulse Gaussian noise using proximal algorithms. The result is then processed by the inverse Anscombe transform. We compare our algorithm to state of the art methods on standard images, and show its superior performance in various noise conditions.