Machine Learning - Special issue on inductive transfer
Learning to learn
International Journal of Computer Vision - Special issue on statistical and computational theories of vision: modeling, learning, sampling and computing, Part I
Limits on Super-Resolution and How to Break Them
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image Denoising Via Learned Dictionaries and Sparse representation
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Learning Sparse Overcomplete Codes for Images
Journal of VLSI Signal Processing Systems
Single-Image Super-Resolution Using Sparse Regression and Natural Image Prior
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image super-resolution via sparse representation
IEEE Transactions on Image Processing
Super-resolution with sparse mixing estimators
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
Joint MAP registration and high-resolution image estimation using a sequence of undersampled images
IEEE Transactions on Image Processing
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
IEEE Transactions on Image Processing
Rate Bounds on SSIM Index of Quantized Images
IEEE Transactions on Image Processing
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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Recent researches have shown that the sparse representation based technology can lead to state of art super-resolution image reconstruction (SRIR) result. It relies on the idea that the low-resolution (LR) image patches can be regarded as down sampled version of high-resolution (HR) images, whose patches are assumed to have a sparser presentation with respect to a dictionary of prototype patches. In order to avoid a large training patches database and obtain more accurate recovery of HR images, in this paper we introduce the concept of examples-aided redundant dictionary learning into the single-image super-resolution reconstruction, and propose a multiple dictionaries learning scheme inspired by multitask learning. Compact redundant dictionaries are learned from samples classified by K-means clustering in order to provide each sample a more appropriate dictionary for image reconstruction. Compared with the available SRIR methods, the proposed method has the following characteristics: (1) introducing the example patches-aided dictionary learning in the sparse representation based SRIR, in order to reduce the intensive computation complexity brought by enormous dictionary, (2) using the multitask learning and prior from HR image examples to reconstruct similar HR images to obtain better reconstruction result and (3) adopting the offline dictionaries learning and online reconstruction, making a rapid reconstruction possible. Some experiments are taken on testing the proposed method on some natural images, and the results show that a small set of randomly chosen raw patches from training images and small number of atoms can produce good reconstruction result. Both the visual result and the numerical guidelines prove its superiority to some start-of-art SRIR methods.