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 modeled 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 regularization terms of the sparse coding framework to characterize 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 analyzed. Extensive
experiments demonstrate that the proposed TWSC scheme outperforms
state-of-the-art denoising methods on removing realistic noise