Self-similarity based structural regularity for just noticeable difference estimation
Journal of Visual Communication and Image Representation
Fast image deconvolution using closed-form thresholding formulas of Lq(q=12,23) regularization
Journal of Visual Communication and Image Representation
A restoration algorithm for images contaminated by mixed Gaussian plus random-valued impulse noise
Journal of Visual Communication and Image Representation
Image upscaling using multiple dictionaries of natural image patches
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part III
Adaptive regularization-based space-time super-resolution reconstruction
Image Communication
An image super-resolution scheme based on compressive sensing with PCA sparse representation
IWDW'12 Proceedings of the 11th international conference on Digital Forensics and Watermaking
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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 the l1-norm optimization techniques and the fact that natural images are intrinsically sparse in some domains. 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 precollected 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 nonlocal 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.