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Image blind denoising system

An image and sample image technology, applied in the field of image denoising, can solve problems such as poor feasibility and achieve the effect of avoiding dependence

Active Publication Date: 2020-09-08
HARBIN INST OF TECH
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Problems solved by technology

[0004] Aiming at the problem that existing image denoising methods rely on noise and clear image pairs for model training, and the feasibility is poor, the present invention provides a blind image denoising system

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specific Embodiment approach 1

[0039] Specific implementation mode 1. Combination figure 1 with Figure 11 As shown, the present invention provides a blind image denoising system, including a self-supervised learning module and a knowledge distillation module;

[0040] The self-supervised learning modules include:

[0041] The blind spot network based on hole convolution and the image-related noise level estimation network are used to optimize the noise sample image in the noise sample image set through self-supervised loss, and obtain a preliminary blind denoising image based on Bayesian prediction, and obtain the noise sample image a first data set consisting of pairs of preliminary blind denoised images;

[0042] The image-related noise level estimation network is further used to process the clear sample images in the clear sample image set, generate corresponding noise images, and obtain a second data set composed of clear sample images and corresponding noise image pairs;

[0043] The knowledge dist...

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Abstract

The invention discloses an image blind denoising system, and belongs to the technical field of image denoising. The method aims to solve the problem that an existing image denoising method depends onnoise and clear image pairs for model training, and feasibility is poor. A self-supervised learning module comprises a blind spot network based on hole convolution and a noise level estimation networkrelated to an image, and is used for carrying out self-supervised loss optimization on a noise sample image in a noise sample image set and obtaining a preliminary blind denoised image based on Bayesian prediction to obtain a first data set; an image-related noise level estimation network further processes the clear sample images in the clear sample image set to generate corresponding noise images, and a second data set composed of the clear sample images and the corresponding noise image pairs is obtained; and a knowledge distillation module is used for training a convolutional neural denoising network based on multistage wavelets by adopting the first data set and the second data set in a fully supervised mode to obtain a denoising model. According to the system, blind denoising is realized based on non-paired images.

Description

technical field [0001] The invention relates to an image blind denoising system and belongs to the technical field of image denoising. Background technique [0002] Image denoising aims to remove the noise in the image, so that the image including noise can be restored to obtain a high-quality and clear image. The main reasons for the noise in the image include the limitations of hardware conditions, the noise generated during transmission and storage, and so on. In applications such as medical images, monitoring and entertainment, higher requirements are often placed on image quality. Therefore, image denoising has always been a research hotspot in the field of image processing. [0003] In recent years, Convolutional Neural Network (CNN) has been successfully used in Gaussian denoising technology and has been continuously improved, aiming to handle more complex noise types, even real noise. However, in order to achieve a considerable denoising effect, the Gaussian denois...

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Application Information

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IPC IPC(8): G06T5/00G06N3/04G06N3/08
CPCG06N3/08G06T2207/10004G06N3/045G06T5/70
Inventor 武小荷刘铭曹越任冬伟左旺孟
Owner HARBIN INST OF TECH
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