Image noisy point detection method based on convolution neural network

A technology of convolutional neural network and detection method, applied in biological neural network models, image enhancement, image data processing, etc., can solve problems such as limited functions, achieve accurate detection results and improve learning accuracy

Active Publication Date: 2014-08-06
XIAMEN MEITUZHIJIA TECH
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Problems solved by technology

[0002] The usual denoising algorithm requires an input parameter, that is, noise intensity; in order to achieve automatic denoising, it is necessary to automatically estimate the noise intensity; existing algorithms usually assume the type of noise

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  • Image noisy point detection method based on convolution neural network
  • Image noisy point detection method based on convolution neural network

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[0018] In order to make the technical problems, technical solutions and beneficial effects to be solved by the present invention more clear and comprehensible, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention, but not to limit the present invention.

[0019] like figure 1 As shown, a convolutional neural network-based image noise detection method of the present invention includes the following steps:

[0020] 10. Collect sample images and manually label and classify them according to the type of noise;

[0021] 20. Normalize the classified sample images and input them into the convolutional neural network system to train the classification model;

[0022] 30. The system randomly collects sample image blocks in the target area of ​​the sample image, and performs noise classification; ...

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Abstract

The invention discloses an image noisy point detection method based on a convolution neural network. The method includes the steps that sample images are collected, manual labeling and classification is conducted according to the types of noisy points, the sample images are input in the convolution neural network for training of classification models, and sample image blocks which are wrongly classified are collected in the classification process for secondary learning classification. The noisy points are labeled and classified in the manual and machine combination mode, supervised learning is achieved, learning accuracy of the convolution neural network is improved, and therefore the noisy points can be directly classified through the best trained classification model in the image noisy point detection process, and detection results are more accurate.

Description

technical field [0001] The invention relates to an image processing method, in particular to an image noise detection method based on a convolutional neural network. Background technique [0002] The usual denoising algorithm requires an input parameter, that is, noise intensity; in order to achieve automatic denoising, it is necessary to automatically estimate the noise intensity; existing algorithms usually assume the type of noise, such as Gaussian white noise, salt and pepper noise, blue noise; and The noise generated by the actual sensor does not conform to these statistical laws, so the effect of this type of algorithm for estimating the intensity of the noise is limited. Contents of the invention [0003] In order to solve the above problems, the present invention provides an image noise detection method based on a convolutional neural network, and the detection result is more accurate. [0004] To achieve the above object, the technical solution adopted in the pre...

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Application Information

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IPC IPC(8): G06T5/00G06N3/02
Inventor 张伟傅松林王喆张长定
Owner XIAMEN MEITUZHIJIA TECH
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