Multitask dictionary learning and sparse representation based single-image super-resolution reconstruction

Image Restoration Using Group-based Sparse Representation Technique

“…The proposed algorithm exhibits superiority over traditional algorithms and recently proposed ones in terms of visual quality, Peak Signal to Noise Ratio (PSNR) and Mean Absolute Error (MAE). Yang et al [6] have introduced the concept of examplesaided redundant dictionary learning into the single-image super-resolution reconstruction, and have proposed multiple dictionaries learning scheme inspired by multitask learning, in order to avoid a large training patches database and obtain more accurate recovery of HR images. Chen et al [7] have designed a new sparsitybased algorithm for the classification of hyper spectral imagery.…”

Bilevel optimization of block compressive sensing with perceptually nonlocal similarity

“…To distinguish the image sub-blocks with different characteristics, clustering is commonly applied and similar sub-blocks are grouped in each cluster. Learning a local dictionary from a cluster of similar image samples has been proven to be efficient in image denoising [14] and image superresolution reconstruction [47] . However, it has not been widely applied in CS.…”

“…In addition, the learned dictionary from a particular group of image samples has been proved to be effective in many applications of image processing such as denoising [14] and superresolution [47] . For BCS, there exist significant differences among various image sub-blocks.…”

A sparse representation based approach for super-resolution reconstruction over geometric dictionaries and consistency of gradients

“…Clustering techniques are then added into the sparse representation based SISR scheme in order to employ the priors of the extracted raw patches. E.g., Yang et al [15] learn numerous dictionaries from several groups of patches produced by K-means algorithm and validate its superiority experimentally.…”