Image super-resolution based on clustering and collaborative representation

Wang Ronggui; Liu Leilei; Yang Juan; Xue Lixia; Hu Min

doi:10.12086/oee.2018.170537

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Wang Ronggui, Liu Leilei, Yang Juan, et al. Image super-resolution based on clustering and collaborative representation[J]. Opto-Electronic Engineering, 2018, 45(4): 170537. doi: 10.12086/oee.2018.170537

Citation:

Wang Ronggui, Liu Leilei, Yang Juan, et al. Image super-resolution based on clustering and collaborative representation[J]. Opto-Electronic Engineering, 2018, 45(4): 170537. doi: 10.12086/oee.2018.170537

Image super-resolution based on clustering and collaborative representation

School of Computer and Information, Hefei University of Technology, Hefei, Anhui 230009, China

Fund Project: Supported by National Natural Science Foundation of China (61672202)

More Information

Corresponding author: Xue Lixia, E-mail: xlxzzm@163.com

Received Date 10 October 2017

Revised Date 05 February 2018

Published Date 01 April 2018

Abstract

Abstract

Image super-resolution (SR) refers to the reconstruction of a high-resolution (HR) image from single or multiple observed degraded low-resolution (LR) images for the purpose of improving image's visual effects and getting more available information. We propose an image super-resolution algorithm based on collaborative representation and clustering in this paper. In the training stage, the image samples are clustered according to the image features and multiple dictionaries are trained by using the differences of image features, which overcomes the shortcoming of lack of expressiveness of traditional single-dictionary training methods. Moreover, projection matrices between different HR and LR image clustering are computed via collaborative representation, which accelerate the speed of image reconstruction. Experiments demonstrate that compared with other methods, the proposed method not only enhanced PSNR and SSIM metrics for reconstructed images but also improved image's visual effects.
- image super-resolution /
- clustering /
- collaborative representation /
- projection matrices

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References

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Overview

Overview

Overview: Image super-resolution (SR), as an important branch of digital image processing and computer vision, has been widely used in video surveillance, medical imaging and remote sensing image processing in these years. The single-image super-resolution (SISR) is currently a very active area of SR research and we focus on it in this paper. The SISR refers to the reconstruction of a high-resolution (HR) image from an observed degraded low-resolution (LR) image. In this paper, we propose a novel single image super-resolution algorithm by combining clustering and collaborative representation. In the training stage of our method, image patches with similar characteristics are clustered into one sub-class by using k-means clustering algorithm firstly, and then use clustered image patches to learn LR dictionary for each sub-class through K-SVD method. The HR dictionary is computed by using the sparse representation theory which assume that the LR and HR image patches have the same sparse coefficient according to corresponding LR and HR dictionaries. Finally, we compute the projection matrix for each clustered dictionary atom based on the CR with l₂-norm regularization, which map LR features onto corresponding HR features. In the reconstruction process, for each input LR patch we first find the nearest LR cluster center and then obtain the projection matrix via closet dictionary atom in the corresponding LR dictionary. The HR patch can be estimated by multiplying input LR patch and the projection matrix at last. In conclusion, more expressiveness dictionaries and more effective projection matrices are obtained via our method.Experimental results demonstrate that our proposed algorithm is both quantitatively and qualitatively superior to other classic image SR algorithms. The visual results of HR images show that our proposed algorithm reconstructs more fine details along the dominant edges and suppresses the unexpected artifacts comparing with other methods.