Abstract：In recent years， learning methods based on deep convolutional neural network have made unprecedented achievements in image noise reduction. Adjusting network structure and parameters to obtain better image noise reduction effect has become a research hotspot. Denoising convolutional neural networkadopts residual learning method in deep neural network， which solves the problem of blind denoising to some extent while improving the effect of denoising. Its shortcomings is that the algorithm convergence time is long. In this paper， an image denoising algorithm based on deconvolution denoising neural networkis proposed. The main features of this paper are as follows. 1) In the original network structure， deconvolution neural network is introduced to optimize the residual learning mode. 2) A new loss function calculation method is proposed. BSD68 and SET12 test data sets are used to verify the method proposed in this paper. Experimental results show that the denoising performance of the algorithm in this paper is 120%-138% shorter than that of denoising convolutional neural network algorithm under the same denoising effect. At the same time， compared with the traditional deep-learning image denoising algorithm， the denoising effect and operation efficiency of this method are also greatly improved.
2020, Vol. 54 
