A depth convolution neural network image denoising method based on Inception model

A deep convolution, neural network technology, applied in the field of image processing, can solve the problems of denoising performance, difficulty, and denoising effect.

CN109214990AInactive Publication Date: 2019-01-15GUANGDONG UNIV OF TECH
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Publication Date
2019-01-15
Estimated Expiration
Not applicable · inactive patent

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Abstract

The invention relates to the technical field of image processing, more specifically, to a depth convolution neural network image denoising method based on an Inception model. Building a training network with one-layer Inception model; setting the parameters of network solution and choosing the appropriate supervisory framework; the parameters of the network structure are optimized, and the obtained network is used for denoising. The depth convolution neural network image denoising method based on the Inception model of the invention is based on the depth convolution neural network and combinesGaussian white noise to simulate unknown real noise and the network containing the Inception model to reduce the parameter quantity on the basis of maintaining the original denoising effect; throughthe way of data expansion, the content of training data is enriched, and the neural network can learn the inner structure of image distribution or noise. By introducing the residual structure, the network converges more easily and the effect is the best.
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Description

technical field

[0001] The present invention relates to the technical field of image processing, and more specifically, to an image denoising method based on a deep convolutional neural network based on an Inception model. Background technique

[0002] With the deepening of the digital revolution, digital images have become an indispensable part of people's lives. In the process of acquiring images, due to the limitations of the imaging system, the acquired images are often noisy; for example, thermal noise due to the internal resistance of the electronic components of the imaging system will be affected by temperature; remote sensing satellite images, because space Random noise due to electromagnetic interference. When the noise intensity reaches a certain level, the image quality will be severely degraded. This makes subsequent information dissemination and image processing difficult. Therefore, image denoising technology is an indispensable research topic in the field ...

Examples

no. 1 example

[0044] Such as Figure 1 to Figure 4 Shown is the first embodiment of the image denoising method based on the deep convolutional neural network model of the Inception model of the present invention, comprising the following steps:

[0045] S1. Select a data set in the source image set, and preprocess the data in the data set to obtain a noisy grayscale image used as input;

[0046] S2. Build and train a deep convolutional neural network with an Inception model layer, use the noisy grayscale image in step S1 as input, use Gaussian white noise to simulate real noise, and denoise through the deep convolutional neural network, the output is denoising image;

[0047] S3. Input the denoised image and the actual clear image in step S2 into the supervisory framework, obtain the gap between the denoised image and the actual clear image, and optimize the deep convolutional neural network in step S2 through a reverse iterative algorithm to reduce small loss function;

[0048] S4. Inpu...