A Trilateral Weighted Sparse Coding Scheme for Real-World Image Denoising

TL;DR

This paper introduces a trilateral weighted sparse coding scheme that effectively denoises real-world images by modeling complex noise characteristics, outperforming existing methods.

Contribution

The paper proposes a novel TWSC framework with three weight matrices to better characterize realistic noise and image priors in denoising tasks.

Findings

TWSC outperforms state-of-the-art methods on real-world noisy images.

The algorithm converges and has a unique solution.

Extensive experiments validate the effectiveness of TWSC.

Abstract

Most of existing image denoising methods assume the corrupted noise to be additive white Gaussian noise (AWGN). However, the realistic noise in real-world noisy images is much more complex than AWGN, and is hard to be modelled by simple analytical distributions. As a result, many state-of-the-art denoising methods in literature become much less effective when applied to real-world noisy images captured by CCD or CMOS cameras. In this paper, we develop a trilateral weighted sparse coding (TWSC) scheme for robust real-world image denoising. Specifically, we introduce three weight matrices into the data and regularisation terms of the sparse coding framework to characterise the statistics of realistic noise and image priors. TWSC can be reformulated as a linear equality-constrained problem and can be solved by the alternating direction method of multipliers. The existence and uniqueness of the solution and convergence of the proposed algorithm are analysed. Extensive experiments demonstrate that the proposed TWSC scheme outperforms state-of-the-art denoising methods on removing realistic noise.