International Journal of Computer Vision - Special issue on statistical and computational theories of vision: modeling, learning, sampling and computing, Part I
Example-Based Super-Resolution
IEEE Computer Graphics and Applications
Limits on Super-Resolution and How to Break Them
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Non-Local Algorithm for Image Denoising
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
IEEE Transactions on Image Processing
CGIV '09 Proceedings of the 2009 Sixth International Conference on Computer Graphics, Imaging and Visualization
Image super-resolution via sparse representation
IEEE Transactions on Image Processing
-SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation
IEEE Transactions on Signal Processing
IEEE Transactions on Information Theory
Near-Optimal Signal Recovery From Random Projections: Universal Encoding Strategies?
IEEE Transactions on Information Theory
IEEE Transactions on Image Processing
New edge-directed interpolation
IEEE Transactions on Image Processing
Image quality assessment: from error visibility to structural similarity
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
Rate Bounds on SSIM Index of Quantized Images
IEEE Transactions on Image Processing
Single image super-resolution based on space structure learning
Pattern Recognition Letters
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Recent development on Compressive Sampling (or compressive sensing, CS) theory suggests that High-Resolution (HR) images can be correctly recovered from their Low-Resolution (LR) version under mild conditions. Inspired by it, we proposed a CS based Single-Image Super-resolution Reconstruction (SISR) framework that exploits the dual-sparsity and non-local similarity constraints of images. This new framework relies on the idea that LR image patch can be regarded as the compressive measurement of its corresponding HR patch, and a sufficiently sparse coding of HR patch under some dictionary will make an accurate recovery of HR patch from its measurement possible. In order to adaptively tune the dictionary that can well represents the underlying HR patches, we reduce the SISR to a dual-sparsity constrained optimization problem with dual variables. Moreover, the pixel based recovery is incorporated as another regularization term to exploit the image non-local similarities, which is very helpful in preserving edge sharpness. The optimization is implemented in a patch-pixel-collaboration and iterative manner, via the Singular Value Decomposition (SVD) and Orthogonal Matching Pursuit (OMP) algorithm. Experiments are taken on some natural images, remote sensing images and medical images, and the results show that our proposed method can not only provide one possible way of recovering HR image under the CS framework, but also generate HR images that are competitive or even superior in quality to images produced by other similar SISR methods.