Real image blind denoising method based on deep residual network

Abstract

The invention provides a real image blind denoising method based on a deep residual network. According to the method, an RGB spatial clear image set is selected through an image dataset, and an RGB spatial image group set is constructed through spatial transformation; images under multiple scenes are shot through multiple cameras, and real image groups are constructed according to real clear images and real noisy images shot by each camera under each scene, and a real image group set is constructed; multiple RGB spatial image groups in the RGB spatial image group set and multiple real image groups in the real image group set are randomly selected to construct an image training set, and a preprocessed image training set is obtained through preprocessing; remaining RGB spatial image groups in the RGB spatial image group set and remaining real image groups in the real image group set are used to construct an image test set; and the preprocessed image training set is used as input to construct an image denoising residual convolutional neural network, the neural network is trained in combination with residual learning and a batch normalization strategy, and the image test set is denoised. The method has the advantages that convergence speed is high, and the denoising effect is good.

Description

technical field [0001] The invention belongs to the fields of digital image processing and computer vision, and in particular relates to a real image blind denoising method based on a deep residual network. Background technique [0002] Image denoising is an important research field in digital image processing and computer vision. The purpose of image denoising is to improve the image quality, better restore the information carried by the image, and provide a basis for further analysis and understanding of the image. [0003] At present, the traditional denoising algorithms mainly include filtering method, non-local method and sparse representation method. Although these algorithms have achieved certain results, there are still some problems in the denoising task: such algorithms usually need to set the noise model in advance, The denoising effect of the algorithm has a great correlation with the noise model adopted. A denoising algorithm, which is effective for the type o...

AI Technical Summary

Problems solved by technology

[0003] At present, the traditional denoising algorithms mainly include filtering method, non-local method and sparse representation method. Although these algorithms have achieved certain results, there are still some problems in the denoising task: such algorithms usually need to set the noise model in advance, The denoising effect of the algorithm has a great correlation with the noise model used

[0005] After searching the literature of the existing technology, it was found that the Chinese published patent "A method for denoising and enhancing deep images based on deep learning" (publication number CN105825484A, the publication date is 2016.08.03) by constructing a three-layer convolution unit Deep image denoising and enhanced convolutional neural network for image denoising and enhancement. However, the image denoising effect and efficiency of this patent can be further improved. Its specific shortcomings are: this patent only uses a 3-layer network structure, Its nonlinear feature representation ability and image reconstruction ability are limited; the training data of this patent are clear images and artificially noisy images, which do not contain real noisy images, and the denoising effect on real noisy images is poor; The low-frequency information of the clear image is reconstructed during the patented network training process, and the high-frequency noise is not directly reconstructed specifically, the model is difficult to converge, and the denoising effect of the image is not good

Images