  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.
