Image noisy point detection method based on convolution neural network

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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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, 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

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