Group Sparsity Mixture Model and Its Application on Image Denoising.

Prior learning is a fundamental problem in the field of image processing. In this paper, we conduct a detailed study on (1) how to model and learn the prior of the image patch group, which consists of a group of non-local similar image patches, and (2) how to apply the learned prior to the whole image denoising task. To tackle the first problem, we propose a new prior model named Group Sparsity Mixture Model (GSMM). With the bilateral matrix multiplication, the GSMM can model both the local feature of a single patch and the relation among non-local similar patches, and thus it is very suitable for patch group based prior learning. This is supported by the parameter analysis which demonstrates that the learned GSMM successfully captures the inherent strong sparsity embodied in the image patch group. Besides, as a mixture model, GSMM can be used for patch group classification. This makes the image denoising method based on GSMM capable of processing patch groups flexibly. To tackle the second problem, we propose an efficient and effective patch group based image denoising framework, which is plug-and-play and compatible with any patch group prior model. Using this framework, we construct two versions of GSMM based image denoising methods, both of which outperform the competing methods based on other prior models, e.g., Field of Experts (FoE) and Gaussian Mixture Model (GMM). Also, the better version is competitive with the state-of-the-art model based method WNNM with about ×8 faster average running speed.