NDLI: Image super-resolution via adaptive sparse representation and self-learning

Content Provider	IET Digital Library
Author	Zhao, Jianwei Sun, Tiantian Cao, Feilong
Abstract	This study proposes a novel super-resolution regularisation model based on adaptive sparse representation and self-learning frameworks. The fidelity term in the model ensures that the reconstructed image is consistent with the observation image. The adaptive sparsity regularisation term constrains the reconstructed image with an adaptive sparse representation, which successfully harmonises the sparse representation and the collaborative representation adaptively via producing suitable coefficients. To construct a more effective dictionary, the high-frequency features from the underlying image patches are extracted, and the dictionary learning and sparse representation are integrated. To this end, the alternating minimisation algorithm is used to divide this model into three subproblems, and the alternating direction method of multipliers and iterative back-projection method are used to solve the subproblems. To illustrate the effectiveness of the proposed method, additional experiments are conducted on some generic images. Compared with some state-of-the-art algorithms, the experimental results demonstrate that the proposed method achieves better results in terms of both visual quality and noise immunity.
Starting Page	753
Ending Page	761
Page Count	9
ISSN	17519632
Volume Number	12
e-ISSN	17519640
Issue Number	Issue 5, Aug (2018)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-cvi/12/5
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-cvi.2017.0153
Journal	IET Computer Vision
Publisher Date	2018-03-19
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Adaptive Sparse Representation Adaptive Sparsity Regularisation Term Collaborative Representation Computer Vision And Image Processing Technique Dictionaries Generic Image Image Reconstruction Image Representation Image Resolution Image Restoration Interpolation And Function Approximation Iterative Method Learning in AI Minimisation Novel Super-resolution Regularisation Model Numerical Analysis Observation Image Optical, Image And Video Signal Processing Reconstructed Image Underlying Image Patches
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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