Improved Super-Resolution Image Reconstruction Algorithm

Aiming at the problems of super-resolution convolutional neural network (SRCNN) with fewer convolutional layers, long training time, difficulty in convergence, and limited expression and generalization capabilities, a residual deconvolution SRCNN (RD-SRCNN) algorithm is proposed in this work. First, different size convolution kernels are used for convolution operation to better extract the detailed features in low resolution images. Then, the acquired image features are input into the residual network composed of convolution layer composed of convolution kernels of different sizes and activation layer of exponential linear unit, and each feature extraction unit is connected by short path to solve the problem of gradient disappearance and realize the feature reuse, and reduce the network redundancy. Finally, a clear high-resolution image is obtained by adding a deconvolution layer to increase the receptive field. Experimental results show that the RD-SRCNN algorithm achieves good results in both visual and objective evaluation criteria.