Image blind denoising method for noise modeling based on capsule generative adversarial network

A noise and capsule technology, applied in the field of computer vision, to achieve the effect of enhanced training set data and excellent effect

Inactive Publication Date: 2021-01-08
HEFEI UNIV OF TECH
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  • Abstract
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  • Application Information

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Problems solved by technology

[0006] In order to solve the shortcomings of the above-mentioned prior art, the present invention proposes an image blind denoising method based on noise modeling of capsule generation confrontation network, in order to be able to solve problems such as the noise information in the image is not available or the sensor In the case of uncertainty, it is still possible to achieve effective image noise reduction and improve the noise reduction effect

Method used

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  • Image blind denoising method for noise modeling based on capsule generative adversarial network
  • Image blind denoising method for noise modeling based on capsule generative adversarial network
  • Image blind denoising method for noise modeling based on capsule generative adversarial network

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Embodiment

[0079] In order to verify the effectiveness of the method of the present invention, this example adopts mixed noise to verify the noise reduction effect of this method, in this example, the mixed noise that adopts is by 10% uniform noise (distribution interval is [-s, s], 20 % Gaussian noise with a variance of 1 and 70% Gaussian noise with a variance of 0.01, and the peak signal-to-noise ratio (PSNR) is used as the evaluation index, as shown in Table 1.

[0080] Table 1

[0081]

[0082] As can be seen from the experimental results in Table 1, the method of the present invention, in terms of mixed noise denoising, no matter it is the existing method BM3D, WNNM, EPLL under the noise non-blind mode, or the existing method under the noise blind denoising mode Multisclale, DnCNN, and peak signal-to-noise ratio are all higher than these methods, showing the superiority of this method.

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Abstract

The invention discloses an image blind denoising method for noise modeling based on a capsule generative adversarial network, and the method comprises the steps: 1, extracting a smooth noise block from a given noise image; 2, carrying out the noise modeling based on the capsule generative adversarial network; and 3, carrying out the training of a deep CNN, and obtaining a noise reduction model, soas to achieve the blind denoising of an image. According to the method, the defect that the noise reduction effect is poor under the condition that noise information is unknown or a sensor is uncertain in the conventional method can be overcome, so that the noise reduction effect is improved.

Description

technical field [0001] The invention belongs to the field of computer vision, in particular to an image blind denoising method based on noise modeling of a capsule generation confrontation network. Background technique [0002] Image denoising is a classic topic in low vision vision and an important preprocessing step in many vision tasks. Following the degradation model y=x+v, the goal of image denoising is to restore a noise-free image x from noisy observations y by reducing noise v. There are basically three types of existing denoising methods: denoising methods based on image priors, blind denoising methods based on noise modeling, and denoising methods based on discriminative learning. [0003] The image priors adopted by image prior-based denoising methods are mainly defined based on human knowledge and may limit the denoising performance; moreover, most methods only utilize the input image when modeling image priority internal information from other images without m...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T5/00G06N3/04G06N3/08
CPCG06T5/002G06N3/08G06T2207/10004G06T2207/20081G06T2207/20084G06N3/048G06N3/045
Inventor 史明光汤亚晨
Owner HEFEI UNIV OF TECH
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