Single image super-resolution using sparse representations with structure constraints

Julio Cesar Ferreira 1, * Olivier Le Meur 1 Christine Guillemot 1 Eduardo Antonio Barros da Silva 2 Gilberto Arantes Carrijo 3
* Corresponding author
1 Sirocco - Analysis representation, compression and communication of visual data
Inria Rennes – Bretagne Atlantique , IRISA-D5 - SIGNAUX ET IMAGES NUMÉRIQUES, ROBOTIQUE
2 COPPE - SMT
UFRJ - Universidade Federal do Rio de Janeiro
3 FEELT
UFU - Federal University of Uberlândia [Uberlândia]
Abstract : This paper describes a new single-image super-resolution algorithm based on sparse representations with image structure constraints. A structure tensor based regularization is introduced in the sparse approximation in order to improve the sharpness of edges. The new formulation allows reducing the ringing artefacts which can be observed around edges reconstructed by existing methods. The proposed method, named Sharper Edges based Adaptive Sparse Domain Selection (SE-ASDS), achieves much better results than many state-of-the-art algorithms, showing significant improvements in terms of PSNR (average of 29.63, previously 29.19), SSIM (average of 0.8559, previously 0.8471) and visual quality perception.
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https://hal.inria.fr/hal-00995052
Contributor : Julio Cesar Ferreira <>
Submitted on : Thursday, May 22, 2014 - 3:56:41 PM
Last modification on : Friday, November 16, 2018 - 1:39:31 AM

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  • HAL Id : hal-00995052, version 1

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Julio Cesar Ferreira, Olivier Le Meur, Christine Guillemot, Eduardo Antonio Barros da Silva, Gilberto Arantes Carrijo. Single image super-resolution using sparse representations with structure constraints. IEEE International Conference on Image Processing, Oct 2014, Paris, France. ⟨hal-00995052⟩

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