A two-stage denoising filter: the preprocessed Yaroslavsky filter

TL;DR

This paper introduces a two-stage image denoising method combining classical prefiltering with a modified Yaroslavsky filter, achieving strong performance and efficiency on cartoon images with theoretical backing.

Contribution

The paper presents a novel two-stage denoising framework that integrates prefiltering with a modified Yaroslavsky filter, providing theoretical guarantees and practical efficiency.

Findings

Effective noise removal on cartoon images

Faster computation compared to patch-based methods

Theoretical analysis supports prefiltering effectiveness

Abstract

This paper describes a simple image noise removal method which combines a preprocessing step with the Yaroslavsky filter for strong numerical, visual, and theoretical performance on a broad class of images. The framework developed is a two-stage approach. In the first stage the image is filtered with a classical denoising method (e.g., wavelet or curvelet thresholding). In the second stage a modification of the Yaroslavsky filter is performed on the original noisy image, where the weights of the filters are governed by pixel similarities in the denoised image from the first stage. Similar prefiltering ideas have proved effective previously in the literature, and this paper provides theoretical guarantees and important insight into why prefiltering can be effective. Empirically, this simple approach achieves very good performance for cartoon images, and can be computed much more quickly than current patch-based denoising algorithms.