A Super-Resolution Reconstruction Algorithm Based on Learning Improvement
In view of the problem of edge blurring and slow reconstruction speed in the existing super-resolution reconstruction of learning-based images, this paper proposes an improvement to the original learning-based reconstruction algorithm and applies Markov random fields to image super-resolution. In the rate reconstruction, during the dictionary training phase, training image blocks are randomly selected, and the texture part of the image is learned and reconstructed. The bicubic interpolation method is used to enlarge the image structure part and color information, the final reconstructed image and the interpolation-amplified image are merged. That is the final result. The experimental results show that the peak signal-to-noise ratio (PSNR) is used to objectively evaluate the image reconstruction effect, and it is concluded that the algorithm of this paper reconstructs the image better, and the reconstruction time also has a certain increase.
KeywordsLearning-based Super-resolution reconstruction Image decomposition Markov random field
This work is supported in part by the National Natural Science Foundation China (61601174), in part by the Postdoctoral Research Foundation of Heilongjiang Province (LBH-Q17150), in part by the Science and Technology Innovative Research Team in Higher Educational Institutions of Heilongjiang Province (No. 2012TD007), in part by the Fundamental Research Funds for the Heilongjiang Provincial Universities (KJCXZD201703), and in part by the Science Foundation of Heilongjiang Province of China (F2018026).
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