A Trilateral Weighted Sparse Coding Scheme for Real-World Image Denoising
This addresses the challenge of denoising images with realistic noise from cameras, which is an incremental improvement over existing methods.
The paper tackles the problem of real-world image denoising, where existing methods fail due to complex noise beyond additive white Gaussian noise, and proposes a trilateral weighted sparse coding scheme that outperforms state-of-the-art methods in experiments.
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.