Linear Attention Based Deep Nonlocal Means Filtering for Multiplicative Noise Removal

TL;DR

This paper introduces a linear attention-based deep nonlocal means filtering method for effectively removing multiplicative noise from images, combining deep learning with traditional filtering for improved performance.

Contribution

It proposes a novel linear attention mechanism that linearizes nonlocal means filtering using deep neural networks, reducing computational complexity and enhancing interpretability.

Findings

Outperforms state-of-the-art methods on simulated and real noise data.

Achieves linear complexity in nonlocal filtering operations.

Maintains interpretability close to traditional nonlocal means.

Abstract

Multiplicative noise widely exists in radar images, medical images and other important fields' images. Compared to normal noises, multiplicative noise has a generally stronger effect on the visual expression of images. Aiming at the denoising problem of multiplicative noise, we linearize the nonlocal means algorithm with deep learning and propose a linear attention mechanism based deep nonlocal means filtering (LDNLM). Starting from the traditional nonlocal means filtering, we employ deep channel convolution neural networks to extract the information of the neighborhood matrix and obtain representation vectors of every pixel. Then we replace the similarity calculation and weighted averaging processes with the inner operations of the attention mechanism. To reduce the computational overhead, through the formula of similarity calculation and weighted averaging, we derive a nonlocal filter with linear complexity. Experiments on both simulated and real multiplicative noise demonstrate that the LDNLM is more competitive compared with the state-of-the-art methods. Additionally, we prove that the LDNLM possesses interpretability close to traditional NLM. The source code and pre-trained model are available at https://github.com/ShowiBin/LDNLM.