Multiple dictionary pairs learning and sparse representation-based infrared image super-resolution with improved fuzzy clustering
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Exploring sparse representation to enhance the resolution of infrared image has attracted much attention in the last decade. However, conventional sparse representation-based super-resolution aim at learning a universal and efficient dictionary pair for image representation. However, considering that a large number of different structures exist in an image, it is insufficient and unreasonable to present various image structures with only one universal dictionary pair. In this paper, we propose an improved fuzzy clustering and weighted scheme reconstruction framework to solve this problem. Firstly, the training patches are divided into multiple clusters by joint learning multiple dictionary pairs with improved fuzzy clustering method. The goal of joint learning is to learn the multiple dictionary pairs which could collectively represent all the training patches with smallest reconstruction error. So that the learned dictionary pairs are more precise and mutually complementary. Then, high-resolution (HR) patches are estimated according to several most accurate dictionary pairs. Finally, these estimated HR patches are integrated together to generate a final HR patch by a weighted scheme. Numerous experiments demonstrate that this framework outperforms some state-of-art super-resolution methods in both quantitatively and perceptually.
KeywordsMulti-sensor Super-resolution Sparse representation Infrared image Dictionary learning Multiview representation Fuzzy clustering theory
The research is sponsored by the National Natural Science Foundation of China (Nos. 61701327, 61711540303 and 61473198), also is supported by the Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD) Fund, Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology(CICAEET) Fund.
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Conflict of interest
All the authors declare that they have no conflict of interest.
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