Today I read a paper titled “Image Deblurring and Super-resolution by Adaptive Sparse Domain Selection and Adaptive Regularization”
The abstract is:
As a powerful statistical image modeling technique, sparse representation has been successfully used in various image restoration applications
The success of sparse representation owes to the development of l1-norm optimization techniques, and the fact that natural images are intrinsically sparse in some domain
The image restoration quality largely depends on whether the employed sparse domain can represent well the underlying image
Considering that the contents can vary significantly across different images or different patches in a single image, we propose to learn various sets of bases from a pre-collected dataset of example image patches, and then for a given patch to be processed, one set of bases are adaptively selected to characterize the local sparse domain
We further introduce two adaptive regularization terms into the sparse representation framework
First, a set of autoregressive (AR) models are learned from the dataset of example image patches
The best fitted AR models to a given patch are adaptively selected to regularize the image local structures
Second, the image non-local self-similarity is introduced as another regularization term
In addition, the sparsity regularization parameter is adaptively estimated for better image restoration performance
Extensive experiments on image deblurring and super-resolution validate that by using adaptive sparse domain selection and adaptive regularization, the proposed method achieves much better results than many state-of-the-art algorithms in terms of both PSNR and visual perception