An adaptive image denoising method based on depth learning

Abstract

The invention relates to an adaptive image denoising method based on depth learning, comprising the following steps: step 1) establishing an image set; 2) constructing an adaptive depth convolution neural network; 3) training the adaptive depth convolution neural network: setting the learn rate and momentum parameters of the depth convolution neural network, training the adaptive depth convolutionneural network through the depth learning framework until the train reaches the maximum number of iterations, and generating the trained adaptive depth convolution neural network model; 4) image denoising: inputting the image to be denoised into the trained adaptive depth convolution neural network model to obtain the corresponding residual image, and subtracting the image to be denoised from theresidual image to obtain the denoised image. The method further improves and stabilizes the training performance of the convolution neural network, and ensures the denoising performance, saves time,does not need to manually adjust parameters, and can realize blind denoising of the unknown noise level image.

Description

technical field [0001] The invention belongs to the technical field of image processing, and in particular relates to an adaptive image denoising method based on deep learning. Background technique [0002] During the imaging, storage and transmission of images, due to the influence of sensor defects, bandwidth limitations, code stream loss, environmental noise, lossy compression and digital-to-analog conversion, etc., the visual effect of the image will deteriorate. Then it will have an impact on subsequent in-depth research such as target segmentation, recognition, detection, and tracking, which in turn will interfere with image analysis, description, classification, and interpretation. Especially in the fields of pattern recognition and artificial intelligence that have emerged in recent years, the quality of images plays a decisive role in them. Therefore, it is necessary to analyze the process of denoising the polluted noise to obtain a high-quality image, and it is al...

AI Technical Summary

Problems solved by technology

These denoising methods have three major disadvantages: First, these algorithms generally have complex optimization problems in the testing phase, which sacrifices time to achieve high-performance denoising effects, resulting in a delay in denoising time; second, existing models are generally non-convex and Another part is to manually select parameters to improve denoising performance

Third, the trained models are all for known specific noise values, and blind noise denoising of untrained images cannot be achieved.

However, because they are analysis models based on prior knowledge, specifically, they are limited in obtaining all the feature structures of the image, and manual fine-tuning of parameters is required throughout the training phase. In addition, the models trained by these methods are all for existing Knowing the specific noise level, blind denoising of images with unknown noise level cannot be realized

Even the current optimal DnCNN method [Zhang K, Zuo W, Chen Y, et al.Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for ImageDenoising.[J].IEEE Transactions on Image Processing,2017,26(7):3142 -3155] breaks the traditional denoising method, uses residual learning to complete denoising, and also needs to pre-set the standard deviation of the noisy training image

If all standard deviations of the training images are the same, the method only works for a certain noise level

Therefore, this method cannot achieve blind denoising of images with unknown noise levels.

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