Image Deblurring and Super-resolution by Adaptive Sparse Domain Selection and Adaptive Regularization

Details Image Deblurring and Super-resolution by Adaptive Sparse Domain Selection and Adaptive Regularization Image Deblurring and Super-resolution by Adaptive Sparse Domain Selection and Adaptive Regularization

2010-12-06

arxiv.org/abs/1012.1184v1

Computer Science

Computer Vision and Pattern Recognition

35 pages. This paper is under review in IEEE TIP

Scientific paper

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.

Affiliated with

Also associated with

No associations

Rating

Image Deblurring and Super-resolution by Adaptive Sparse Domain Selection and Adaptive Regularization does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Image Deblurring and Super-resolution by Adaptive Sparse Domain Selection and Adaptive Regularization, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Image Deblurring and Super-resolution by Adaptive Sparse Domain Selection and Adaptive Regularization will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-167931