Multi-Scale Convolutional Neural Network Reconstruction Algorithm Based on Edge Correction

At present, the super-resolution reconstruction methods based on convolutional neural network have the defects of large amount of parameters, low timeliness and loss of edge detail information. In order to solve these problems, we propose a super-resolution reconstruction algorithm of multiscale convolution neural network based on edge correction. Firstly, in the training phase, we set the parameter sharing layer by using the redundancy of low frequency information, In other words, the same set of filters applied to different magnification training networks to build the multi-task learning framework. In the reconstruction phase, the edge correction coefficient of high-resolution image is learned from the sample training library. The neighborhood pixel difference is used to fuse the edge coefficient and the reconstructed high resolution image, and to correct the deviation of the edge information and make up for the missing details. Finally, according to the stochastic gradient descent and back-propagation, we use the gradient to continuously update the weight parameters to make the network reach the maximum optimization. Experimental results show that the proposed algorithm has the significant reconstruction effect, high edge sharpness, elimination of blurring and aliasing, and greatly reduces the amount of parameters through parameter sharing to meet real-time requirements.