MindX: Denoising Mixed Impulse Poisson-Gaussian Noise Using Proximal Algorithms

TL;DR

This paper introduces a new blind image denoising algorithm that effectively removes mixed impulse, Poisson, and Gaussian noise by transforming, optimizing, and inverse transforming the noise, outperforming existing methods.

Contribution

The paper proposes a novel denoising algorithm combining variance stabilization and proximal optimization for mixed noise removal, with superior performance over state-of-the-art methods.

Findings

Outperforms existing denoising methods on standard images

Effective removal of mixed impulse, Poisson, and Gaussian noise

Demonstrates robustness across various noise conditions

Abstract

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.