DocumentCode :
63594
Title :
Single Image Superresolution via Directional Group Sparsity and Directional Features
Author :
Xiaoyan Li ; Hongjie He ; Ruxin Wang ; Dacheng Tao
Author_Institution :
Southwest Jiaotong Univ., Chengdu, China
Volume :
24
Issue :
9
fYear :
2015
fDate :
Sept. 2015
Firstpage :
2874
Lastpage :
2888
Abstract :
Single image superresolution (SR) aims to construct a high-resolution version from a single low-resolution (LR) image. The SR reconstruction is challenging because of the missing details in the given LR image. Thus, it is critical to explore and exploit effective prior knowledge for boosting the reconstruction performance. In this paper, we propose a novel SR method by exploiting both the directional group sparsity of the image gradients and the directional features in similarity weight estimation. The proposed SR approach is based on two observations: 1) most of the sharp edges are oriented in a limited number of directions and 2) an image pixel can be estimated by the weighted averaging of its neighbors. In consideration of these observations, we apply the curvelet transform to extract directional features which are then used for region selection and weight estimation. A combined total variation regularizer is presented which assumes that the gradients in natural images have a straightforward group sparsity structure. In addition, a directional nonlocal means regularization term takes pixel values and directional information into account to suppress unwanted artifacts. By assembling the designed regularization terms, we solve the SR problem of an energy function with minimal reconstruction error by applying a framework of templates for first-order conic solvers. The thorough quantitative and qualitative results in terms of peak signal-to-noise ratio, structural similarity, information fidelity criterion, and preference matrix demonstrate that the proposed approach achieves higher quality SR reconstruction than the state-of-the-art algorithms.
Keywords :
compressed sensing; curvelet transforms; edge detection; feature extraction; image reconstruction; image resolution; LR image; SR reconstruction; curvelet transform; directional group sparsity; directional nonlocal means regularization term; extract directional features; first-order conic solvers; group sparsity structure; image gradients; image pixel; information fidelity criterion; natural images; peak signal-to-noise ratio; sharp edges; similarity weight estimation; single image superresolution; single low-resolution image; total variation regularizer; Dictionaries; Estimation; Image edge detection; Image reconstruction; Image resolution; Training; Transforms; Image super-resolution; directional features; directional group sparsity; reconstruction-based;
fLanguage :
English
Journal_Title :
Image Processing, IEEE Transactions on
Publisher :
ieee
ISSN :
1057-7149
Type :
jour
DOI :
10.1109/TIP.2015.2432713
Filename :
7106514
Link To Document :
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