Convolutional Sparse Coding for Image Super-Resolution

Most of the previous sparse coding (SC) based super resolution (SR) methods partition the image into overlapped patches, and process each patch separately. These methods, however, ignore the consistency of pixels in overlapped patches, which is a strong constraint for image reconstruction. In this p...

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Published in:Proceedings / IEEE International Conference on Computer Vision pp. 1823 - 1831
Main Authors: Gu, Shuhang, Zuo, Wangmeng, Xie, Qi, Meng, Deyu, Feng, Xiangchu, Zhang, Lei
Format: Conference Proceeding Journal Article
Language:English
Published: IEEE 01.12.2015
Subjects:
Algorithms
Coding
Computer vision
Consistency
Convolution
Convolutional codes
Dictionaries
Encoding
Feature maps
Image coding
Image reconstruction
Image resolution
Surface layer
Texture
ISSN:2380-7504
Online Access:Get full text
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Abstract Most of the previous sparse coding (SC) based super resolution (SR) methods partition the image into overlapped patches, and process each patch separately. These methods, however, ignore the consistency of pixels in overlapped patches, which is a strong constraint for image reconstruction. In this paper, we propose a convolutional sparse coding (CSC) based SR (CSC-SR) method to address the consistency issue. Our CSC-SR involves three groups of parameters to be learned: (i) a set of filters to decompose the low resolution (LR) image into LR sparse feature maps, (ii) a mapping function to predict the high resolution (HR) feature maps from the LR ones, and (iii) a set of filters to reconstruct the HR images from the predicted HR feature maps via simple convolution operations. By working directly on the whole image, the proposed CSC-SR algorithm does not need to divide the image into overlapped patches, and can exploit the image global correlation to produce more robust reconstruction of image local structures. Experimental results clearly validate the advantages of CSC over patch based SC in SR application. Compared with state-of-the-art SR methods, the proposed CSC-SR method achieves highly competitive PSNR results, while demonstrating better edge and texture preservation performance.
AbstractList Most of the previous sparse coding (SC) based super resolution (SR) methods partition the image into overlapped patches, and process each patch separately. These methods, however, ignore the consistency of pixels in overlapped patches, which is a strong constraint for image reconstruction. In this paper, we propose a convolutional sparse coding (CSC) based SR (CSC-SR) method to address the consistency issue. Our CSC-SR involves three groups of parameters to be learned: (i) a set of filters to decompose the low resolution (LR) image into LR sparse feature maps, (ii) a mapping function to predict the high resolution (HR) feature maps from the LR ones, and (iii) a set of filters to reconstruct the HR images from the predicted HR feature maps via simple convolution operations. By working directly on the whole image, the proposed CSC-SR algorithm does not need to divide the image into overlapped patches, and can exploit the image global correlation to produce more robust reconstruction of image local structures. Experimental results clearly validate the advantages of CSC over patch based SC in SR application. Compared with state-of-the-art SR methods, the proposed CSC-SR method achieves highly competitive PSNR results, while demonstrating better edge and texture preservation performance.
Author Deyu Meng
Lei Zhang
Wangmeng Zuo
Qi Xie
Xiangchu Feng
Shuhang Gu
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SubjectTerms Algorithms
Coding
Computer vision
Consistency
Convolution
Convolutional codes
Dictionaries
Encoding
Feature maps
Image coding
Image reconstruction
Image resolution
Surface layer
Texture
Title Convolutional Sparse Coding for Image Super-Resolution
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