一种基于双分支改良编解码器的图像去噪算法

Aiming at the problems of the traditional image denoising algorithm such as difficult multi-noise removal, complex deep convolutional neural network denoising model network and long training time, a dual-branch modified codec(DMC) network based on auto-encoder structure was proposed to achieve the high-efficient image denoising. One of the dual branch structure used the down-up sampling to eliminate the point noise, the other focused on the macroscopical image restoration and artifacts removal, and the residual structure was used to integrate at the end to realize the mixed noise denoising of the digital image. The experimental results show that for the image test set of the mixed noise containing Gaussian noise with standard deviation of 15 and mean value of 0, salt and pepper noise as well as shot noise with noise density of 5%, compared with the peak signal-to-noise ratio of the input mixed noise image, the experimental denoising effect is improved by 5.3% on average. Compared with the 12-layer full convolutional neural network, the denoising effect is equivalent and the training speed is increased by about 25.4%, which embodies the advantages of its lightweight. The experimental conclusions indicate that compared with the deep convolution neural network, this method has the advantages of fast training speed and simple network; compared with the traditional image denoising algorithm, it has better noise removal effect. This algorithm can be applied to the end denoising of lightweight vision platform.