Image denoising method based on generative adversarial networks

A network and image technology, applied in the field of computer vision, to achieve the effect of uniform distribution

Inactive Publication Date: 2018-11-06
DALIAN UNIV OF TECH
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  • Application Information

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Problems solved by technology

However, there are still deficiencies in the restoration of image texture details. In order to improve image

Method used

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  • Image denoising method based on generative adversarial networks
  • Image denoising method based on generative adversarial networks
  • Image denoising method based on generative adversarial networks

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Experimental program
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Embodiment

[0086] The denoising of X-ray imaging images is the experimental goal. The experimental platform GPU is NVIDIA GeForce GTX TITANX, and the operating environment is Ubuntu14.04, Python3.4, Tensorflow0.12.1.

[0087] Step 1. Obtain an X-ray imaging picture I, where the pixel gray levels of I range from 0 to 255.

[0088] Step 2. Establish a noise-free image library, and use 500 grayscale images of 180×180 pixel size in the LSUN data set in the network as the experimental data set.

[0089] Step 3. The images in the data set are divided into 98000 image blocks through a sliding window with a step size of 10.

[0090] Step 4, adding Gaussian noise with an intensity of 0 to 50 to the image block to train the noise discrimination network.

[0091] Step 5: Add noise of a certain intensity to the image block to train the generative confrontation network, and save the network parameters corresponding to the noise.

[0092] Step 6. Change the added noise intensity, and repeat step 5 ...

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Abstract

The invention provides an image denoising method based on generative adversarial networks, and belongs to the technical field of computer vision. The method comprises the following steps: (1) designing a neural network for estimation for noise intensity of an image containing noises; (2) using image blocks in an image library to add noises of the intensity according to the estimated noise intensity to use the same as samples of training the networks; (3) in network training, designing a new generation network and discrimination network, and adopting a form of fixing the generation network to train the discrimination network and fixing discrimination network parameters to train the generation network to enable the networks to carry out adversarial training; and (4) using the trained generation network as a denoising network, and selecting a network parameter according to a result, which is obtained by the noise recognition network, to denoise the image containing the noises. The methodhas the effects and the advantages that a visual effect of the denoised image is improved without the need for manual intervention for adjusting the parameter, and texture details of the image can bebetter restored.

Description

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Claims

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Application Information

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Owner DALIAN UNIV OF TECH
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