Correlation preserving on graphs for image denoising
In this paper, we propose a novel dictionary-driven image denoising method based on correlation preserving on graphs. To overcome the drawbacks of the instable and unreliable correlations among a set of learned basis vectors, two effective regularized strategies are employed in our coding process. Specifically, a graph-based regularizer is built to preserve the global similarity, which can adaptively capture both geometric structures and discriminative features of textured patches. In particular, edge weights in the graph are obtained by seeking a nonnegative low-rank construction. Besides, a locality constraint is designed to automatically preserve not only spatial neighborhood information but also internal consistency present in noisy patches while learning an overcomplete dictionary. Experimental results show that our method achieves state-of-the-art denoising results in terms of both PSNR and subjective visual quality.