Aiming at many current image reconstruction algorithms such as JPEG decompression and compressive sensing reconstruction, there are some problems such as unclear image and low resolution. This paper proposes a high-resolution image reconstruction mechanism based on code-decoding symmetric neural network. Firstly, the image is compressed to obtain a low-resolution image, and then the low-resolution image is used as an input image to encode-decode a symmetric neural network, and the convolutional neural network is used to encode the feature image, and finally the deconvolution neural network is used. Decoding implements detail recovery of the image. The experimental results show that the image reconstructed by the code-decoded symmetric neural network is clearer than the previous low-resolution image, indicating that the resolution of the image is improved.