SINGLE IMAGE SUPER-RESOLUTION USING SPARSE REPRESENTATIONS WITH STRUCTURE CONSTRAINTS

被引：0

作者：

Ferreira, J. C. ^{[1
,3
]}

Le Meur, O. ^{[1
]}

Guillemot, C. ^{[1
]}

da Silva, E. A. B. ^{[2
]}

Carrijo, G. A. ^{[3
]}

机构：

[1] INRIA, SIROCCO Res Team, Paris, France

[2] Univ Fed Rio de Janeiro, COPPE, Rio de Janeiro, Brazil

[3] UFU, FEELT, Rio De Janeiro, Brazil

来源：

2014 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP) | 2014年

关键词：

super-resolution; sparse representations; structure tensors;

D O I：

暂无

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

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.

引用

页码：3862 / 3866

页数：5

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