Improving resolution by image registration
CVGIP: Graphical Models and Image Processing
Improved resolution from subpixel shifted pictures
CVGIP: Graphical Models and Image Processing
Biologically motivated computationally intensive approaches to image pattern recognition
Future Generation Computer Systems - Special double issue: high performance computing and networking (HPCN)
Self-similarity properties of natural images
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Approximate nearest neighbor queries in fixed dimensions
SODA '93 Proceedings of the fourth annual ACM-SIAM Symposium on Discrete algorithms
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
Fundamental Limits of Reconstruction-Based Superresolution Algorithms under Local Translation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image upsampling via imposed edge statistics
ACM SIGGRAPH 2007 papers
Stochastic super-resolution image reconstruction
Journal of Visual Communication and Image Representation
New learning based super-resolution: use of DWT and IGMRF prior
IEEE Transactions on Image Processing
New learning based super-resolution: use of DWT and IGMRF prior
IEEE Transactions on Image Processing
Image super-resolution via sparse representation
IEEE Transactions on Image Processing
Image and video upscaling from local self-examples
ACM Transactions on Graphics (TOG)
IEEE Transactions on Information Theory
Joint MAP registration and high-resolution image estimation using a sequence of undersampled images
IEEE Transactions on Image Processing
Image quality assessment: from error visibility to structural similarity
IEEE Transactions on Image Processing
Fast and robust multiframe super resolution
IEEE Transactions on Image Processing
FSIM: A Feature Similarity Index for Image Quality Assessment
IEEE Transactions on Image Processing
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Recent approaches on single image super-resolution (SR) have attempted to exploit self-similarity to avoid the use of multiple images. In this paper, we propose an SR method based on self-learning and Gabor prior. Given a low resolution (LR) test image I0 and its coarser resolution version I−1, both captured from the same camera, we first estimate the degradation between LR and HR (I1) images by constructing the LR-HR patches from LR test image, I0. The HR patches are obtained from I0 by searching for similar patches (of I0) of the same size in I−1. A nearest neighbor search is used to find the best LR match which is then used to obtain the parent HR patch from I0. All such LR-HR patches form self-learned dictionaries. The HR patches that do not find LR match in I−1 are estimated using self-learned dictionaries constructed from the already found LR-HR patches. A compressive sensing-based method is used to obtain the missing HR patches. The estimated LR-HR pairs are used to obtain the LR image formation model by computing the degradation for each pair. A new prior, called Gabor Prior, based on the outputs of a Gabor filter bank is proposed that restricts the solution space by imposing the condition of preserving the SR features at different frequencies. The experimental results show the effectiveness of the proposed approach.