Abstract:

The image super-resolution reconstruction methods based on the convolutional neural network have high reconstruction performance, but they have many network parameters and are difficult to train. Also, they are prone to problems such as gradient disappearance and network degradation. To solve these problems, a super-resolution image reconstruction method based on the symmetric residual convolution neural network is proposed. This method integrates symmetry into residual blocks. It realizes local feature fusion by adopting the symmetric connection and extracts as many valuable features as possible. To improve the quality of image reconstruction, the global feature fusion is realized by skip connection. In this method, the peak signal to noise ratio (PSNR) and structural similarity (SSIM) are used as evaluation indexes. The results by the proposed reconstruction method on Set5, Set14, and BSD100 are superior to those by most of other methods in comparison. The average PSNR and SSIM values are improved compared with those methods. Experimental results show that the image reconstructed by this method has clearer textures, richer details and a better subjective visual effect.

Key words: super-resolution reconstruction, convolution neural network, deep learning, symmetric residual network