Alternatively Constrained Dictionary Learning For Image Superresolution

Dictionaries are crucial in sparse coding-based algorithm for image superresolution. Sparse coding is a typical unsupervised learning method to study the relationship between the patches of high-and low-resolution images. However, most of the sparse coding methods for image superresolution fail to s...

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Vydané v:IEEE transactions on cybernetics Ročník 44; číslo 3; s. 366 - 377
Hlavní autori: Lu, Xiaoqiang, Yuan, Yuan, Yan, Pingkun
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: United States IEEE 01.03.2014
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
Algorithms
Coding
Dictionaries
Encoding
Image coding
Image reconstruction
Image superresolution
Learning
Machine learning
Manifold learning
Mathematical models
nonlocal self-similarity
Preserves
Reconstruction
Sparse approximation
Superresolution
Teaching methods
Training
two-stage dictionary training (TSDT)
Unsupervised learning
ISSN:2168-2267, 2168-2275, 2168-2275
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Shrnutí:Dictionaries are crucial in sparse coding-based algorithm for image superresolution. Sparse coding is a typical unsupervised learning method to study the relationship between the patches of high-and low-resolution images. However, most of the sparse coding methods for image superresolution fail to simultaneously consider the geometrical structure of the dictionary and the corresponding coefficients, which may result in noticeable superresolution reconstruction artifacts. In other words, when a low-resolution image and its corresponding high-resolution image are represented in their feature spaces, the two sets of dictionaries and the obtained coefficients have intrinsic links, which has not yet been well studied. Motivated by the development on nonlocal self-similarity and manifold learning, a novel sparse coding method is reported to preserve the geometrical structure of the dictionary and the sparse coefficients of the data. Moreover, the proposed method can preserve the incoherence of dictionary entries and provide the sparse coefficients and learned dictionary from a new perspective, which have both reconstruction and discrimination properties to enhance the learning performance. Furthermore, to utilize the model of the proposed method more effectively for single-image superresolution, this paper also proposes a novel dictionary-pair learning method, which is named as two-stage dictionary training. Extensive experiments are carried out on a large set of images comparing with other popular algorithms for the same purpose, and the results clearly demonstrate the effectiveness of the proposed sparse representation model and the corresponding dictionary learning algorithm.
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ISSN:2168-2267
2168-2275
2168-2275
DOI:10.1109/TCYB.2013.2256347