Super-resolution reconstruction of images based on uncontrollable microscanning and genetic algorithm
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Abstract:
Aiming at these disadvantages like lack of details, poor contrast and blurry edges of infrared images reconstructed by traditional controllable microscanning super-resolution reconstruction (SRR), this paper proposes a novel algorithm, which samples multiple low-resolution images (LRIs) by uncontrollable microscanning, and then uses LRIs as chromosomes of genetic algorithm (GA). After several generations of evolution, optimal LRIs are got to reconstruct the high-resolution image (HRI). The experimental results show that the average gradient of the image reconstructed by the proposed algorithm is increased to 1.5 times of that of the traditional SRR algorithm, and the amounts of information, the contrast and the visual effect of the reconstructed image are improved.
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This work has been supported by the National Natural Science Foundation of China （No.61275099）, and the Project of Key Laboratory of Signal and Information Processing of Chongqing （No.CSTC2009CA2003）.
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