Image denoising algorithm based on information preservation network

Due to various factors such as imaging equipment, the image will be disturbed by noise during imaging or sensing. Image denoising aims to reduce or eliminate the influence of noise on the image, which often leads to the loss of high-frequency information. In order to protect the edge information and texture details of the image while removing image noise, a convolution neural network with information preservation blocks with relatively low computational complexity is proposed to denoise the noisy image directly. The information preservation block extracts the mixed feature information of the local long path and the local short path by residual learning. Peak signal to noise ratio (PSNR/dB) and structural similarity index method (SSIM) are used to quantify the experimental results. The larger the two indexes, the better the denoising effect. Experiments show that the mean values of PSNR and SSIM can reach 30.36 dB and 0.828 0. Compared with other denoising algorithms, the two evaluation indexes are improved by 2.15 dB and 0.072 9 respectively. The proposed algorithm has good denoising effect for different kinds and different levels of noise, and the speed is better than the general algorithms compared, which contributes to the further development of the denoising based on convolutional neural networks.