Research on Image Super-Resolution Reconstruction Optimization Based on Deep
This paper takes image super-resolution reconstruction as the research object,focuses on the super-resolution convolutional neural network in the deep learning method,and introduces an optimization method based on regularization.This paper firstly studies the basic framework of Super Resolution Convolutional Network(SRCNN),then proposes a regularization optimization method,and finally uses the DIV2K data set to verify the effectiveness of the optimization method in the image reconstruction task.The experimental results show that SRCNN using regularization optimization has achieved significant improvements in both fidelity and structural similarity.
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