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Image de-noising method based on convolution pair neural network

A neural network and neural network model technology, applied in the field of computer vision and digital image processing, can solve the problems of occasions that are not suitable for real-time denoising, the denoising effect is not so good, and the noise cannot be completely removed, so as to improve efficiency and quality , reduce denoising time, and enhance the effect of learning ability

Inactive Publication Date: 2017-02-15
SHENZHEN INST OF FUTURE MEDIA TECH +1
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Problems solved by technology

However, the low-rank process cannot completely remove the noise, so the denoising effect is not so good; in addition, the time complexity is high, and it is not suitable for the occasions that actually need real-time denoising

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  • Image de-noising method based on convolution pair neural network
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  • Image de-noising method based on convolution pair neural network

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Embodiment Construction

[0027] The present invention will be further described below with reference to the accompanying drawings and in combination with preferred embodiments.

[0028] The image denoising method based on the convolution pair neural network of the present invention introduces the convolution layer and the activation layer by introducing the convolution pair neural network, and obtains good features by virtue of the learning ability of the convolution layer and the screening ability of the activation layer, Greatly enhance the learning ability of the neural network, accurately learn the mapping from the noisy image to the clean image to establish the mapping from input to output, so that the prediction and estimation of the clean image can be performed through the learned mapping.

[0029] like figure 1 Shown, the image denoising method of neural network based on the convolution of the preferred embodiment of the present invention, comprises the following steps:

[0030] S1: Build a c...

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Abstract

The invention discloses an image de-noising method based on a convolution pair neural network, comprising the following steps: building a convolution pair neural network model, wherein the convolution pair neural network model includes multiple convolution pairs and corresponding activation layers; selecting a training set, and setting the training parameters of the convolution pair neural network model; according to the convolution pair neural network model and the training parameters thereof, training the convolution pair neural network model with the goal of loss function minimizing to form an image de-noising neural network model; and inputting a to-be-processed image to the image de-noising neural network model, and outputting a de-noised image. Through the image de-noising method based on a convolution pair neural network disclosed by the invention, the learning ability of the neural network is enhanced greatly, accurate mapping from noisy images to clean images is established, and real-time de-noising is realized.

Description

technical field [0001] The invention relates to the fields of computer vision and digital image processing, in particular to an image denoising method based on convolution neural network. Background technique [0002] Image denoising is a classic and fundamental problem in computer vision and image processing. It is a necessary preprocessing process to solve many related problems. Its purpose is to restore a potential clean image x from a noisy image y. The process can be expressed as: y=x+n, where n is usually considered as Additive White Gaussian (AWG), which is a typical ill-conditioned linear inverse problem. In order to solve this problem, many early methods are solved by local filtering, such as Gaussian filtering, median filtering, bilateral filtering, etc. These local filtering methods neither filter in the global scope nor consider the relationship between natural image blocks and The connection between blocks, so the obtained denoising effect is not satisfactory. ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T5/00
CPCG06T2207/20081G06T5/70
Inventor 张永兵孙露露王好谦王兴政李莉华戴琼海
Owner SHENZHEN INST OF FUTURE MEDIA TECH
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