A Blind Denoising Method for Real Images Based on Deep Residual Networks

Abstract

The present invention proposes a real image blind denoising method based on a deep residual network. Select the RGB space clear image set through the image data set, and construct the RGB space image group set through space transformation; use multiple cameras to capture images in multiple scenes, and construct real clear images and real noisy images for each camera and each scene The real image group constructs the real image group set; randomly selects multiple groups of RGB space image groups in the RGB space image group set and multiple groups of real image groups in the real image group set to construct an image training set. The remaining RGB space image group in the image group set and the remaining real image group in the real image group set construct an image test set; the image denoising residual convolutional neural network is constructed by using the preprocessed image training set as input, and the residual learning and batch learning are combined. The normalization strategy trains the neural network and denoises the test set of images. The invention has the advantages of fast convergence speed and good denoising effect.

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...

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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

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