A Simple, General Model for the Affine Self-similarity of Images
ICIAR '08 Proceedings of the 5th international conference on Image Analysis and Recognition
Example-based image super-resolution with class-specific predictors
Journal of Visual Communication and Image Representation
A shrinkage learning approach for single image super-resolution with overcomplete representations
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part II
Super-resolution with sparse mixing estimators
IEEE Transactions on Image Processing
Self-similarity-based image denoising
Communications of the ACM
Variational method for super-resolution optical flow
Signal Processing
Structural similarity-based affine approximation and self-similarity of images revisited
ICIAR'11 Proceedings of the 8th international conference on Image analysis and recognition - Volume Part II
On single image scale-up using sparse-representations
Proceedings of the 7th international conference on Curves and Surfaces
Solving the inverse problem of image zooming using "self-examples"
ICIAR'07 Proceedings of the 4th international conference on Image Analysis and Recognition
Greedy regression in sparse coding space for single-image super-resolution
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
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In super-resolution (SR) reconstruction of images, regularization becomes crucial when insufficient number of measured low-resolution images is supplied. Beyond making the problem algebraically well posed, a properly chosen regularization can direct the solution toward a better quality outcome. Even the extreme case—a SR reconstruction from a single measured image—can be made successful with a well-chosen regularization. Much of the progress made in the past two decades on inverse problems in image processing can be attributed to the advances in forming or choosing the way to practice the regularization. A Bayesian point of view interpret this as a way of including the prior distribution of images, which sheds some light on the complications involved. This paper reviews an emerging powerful family of regularization techniques that is drawing attention in recent years—the example-based approach. We describe how examples can and have been used effectively for regularization of inverse problems, reviewing the main contributions along these lines in the literature, and organizing this information into major trends and directions. A description of the state-of-the-art in this field, along with supporting simulation results on the image scale-up problem are given. This paper concludes with an outline of the outstanding challenges this field faces today.