Due to the low light level and low visibility of night scene, there are many problems in night vision image, such as low signal to noise ratio and low imaging quality. To solve the problems, a dual-channel night vision image restoration method based on deep learning was proposed. Firstly, two Convolutional Neural Network (CNN) based on Fully connected Multi-scale Residual learning Block (FMRB) were used to extract multi-scale features and fuse hierarchical features of infrared night vision images and low-light-level night vision images respectively, so as to obtain the reconstructed infrared image and enhanced low-light-level image. Then, the two processed images were fused by the adaptive weighted averaging algorithm, and the effective information of the more salient one in the two images was highlighted adaptively according to the different scenes. Finally, the night vision restoration images with high resolution and good visual effect were obtained. The reconstructed infrared night vision image obtained by the FMRB based deep learning network had the average values of Peak Signal to Noise Ratio (PSNR) and Structural Similarity (SSIM) by 3.56 dB and 0.091 2 higher than the image obtained by Super-Resolution Convolutional Neural Network (SRCNN) reconstruction algorithm respectively, and the enhanced low-light-level night vision image obtained by the FMRB based deep learning network had the average values of PSNR and SSIM by 6.82dB and 0.132 1 higher than the image obtained by Multi-Scale Retinex with Color Restoration (MSRCR). Experimental results show that, by using the proposed method, the resolution of reconstructed image is improved obviously and the brightness of the enhanced image is also improved significantly, and the visual effect of the fusion image obtained by the above two images is better. It can be seen that the proposed algorithm can effectively restore the night vision images.

Due to the characteristics of water itself and the absorption and scattering of light by suspended particles in the water, a series of problems, such as low Signal-to-Noise Ratio (SNR) and low resolution, exist in underwater images. Most of the traditional processing methods include image enhancement, restoration and reconstruction rely on degradation model and have ill-posed algorithm problem. In order to further improve the effects and efficiency of underwater image restoration algorithm, an improved image super-resolution reconstruction method based on deep convolutional neural network was proposed. An Improved Dense Block structure (IDB) was introduced into the network of the method, which can effectively solve the gradient disappearance problem of deep convolutional neural network and improve the training speed at the same time. The network was used to train the underwater images before and after the degradation by registration and obtained the mapping relation between the low-resolution image and the high-resolution image. The experimental results show that on a self-built underwater image training set, the underwater image reconstructed by the deep convolutional neural network with IDB has the Peak Signal-to-Noise Ratio (PSNR) and Structural SIMilarity (SSIM) improved by 0.38 dB and 0.013 respectively, compared with SRCNN (an image Super-Resolution method using Conventional Neural Network) and proposed method can effectively improve the reconstruction quality of underwater images.