DenseNet-based image noise recognition method and device

A technology of image noise and recognition method, which is applied in the field of image noise recognition, can solve the problem of low recognition accuracy of low-intensity mixed noise, and achieve good recognition effect

Pending Publication Date: 2020-08-28
HAINAN UNIVERSITY
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Problems solved by technology

[0006] Based on this, the purpose of the present invention is to provide a DenseNet-based image noise recognition method and device, which solves the problem of low recognition accuracy of low-intensity mixed noise in the existing image noise recognition technology, and can better identify image noise. Identify the noise type and noise intensity of the low-intensity mixed noise existing in the

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  • DenseNet-based image noise recognition method and device
  • DenseNet-based image noise recognition method and device
  • DenseNet-based image noise recognition method and device

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[0061]Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, the same numerals in different drawings refer to the same or similar elements unless otherwise indicated. The implementations described in the following exemplary examples do not represent all implementations consistent with this specification. Rather, they are merely examples of apparatuses and methods consistent with aspects of the present specification as recited in the appended claims.

[0062] The terms used in this specification are for the purpose of describing particular embodiments only, and are not intended to limit the specification. As used in this specification and the appended claims, the singular forms "a", "the", and "the" are intended to include the plural forms as well, unless the context clearly dictates otherwise. It should also be understood that the te...

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Abstract

The invention relates to a DenseNet-based image noise recognition method and a DenseNet-based image noise recognition device. The DenseNet-based image noise recognition method comprises the steps of obtaining a noiseless image, and adding a plurality of noises of set types and set intensities into the noiseless image to obtain a noise image training set; extracting a gray probability distributioncurve of the noise image to obtain a first visual statistical characteristic image; inputting the first visual statistical feature image as a training sample into an image noise recognition network model; extracting a gray scale curve of the to-be-detected noise image to obtain a second visual statistical characteristic image; and inputting the second visual statistical characteristic image into the trained image noise recognition network model to obtain a noise type and an intensity estimation value of the noise image to be detected. According to the method, the problem of low recognition accuracy of low-intensity mixed noise in the existing image noise recognition technology is solved, and the noise type and the noise intensity of the low-intensity mixed noise existing in the image can be well recognized.

Description

technical field [0001] The present invention relates to the technical field of image noise recognition, in particular to a DenseNet-based image noise recognition method and device. Background technique [0002] Image noise refers to unnecessary or redundant interference information existing in image data. The existence of noise seriously affects the quality of the image, so it must be corrected before image enhancement processing and classification processing. [0003] The identification of image noise plays a vital role in the field of image processing, which will directly affect the subsequent image processing. The acquisition of image signals needs to go through the steps of acquisition, transmission, storage, preprocessing, and display. The process is cumbersome and each step is independent of each other, so it is difficult to accurately analyze the noise in the image. [0004] In practical applications, such as in the medical context, CT images will inevitably be affe...

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Application Information

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IPC IPC(8): G06K9/62G06N3/04G06N3/08
CPCG06N3/08G06N3/045G06F18/2415G06F18/214Y02T90/00
Inventor 张雨黄梦醒倪泽浩冯文龙
Owner HAINAN UNIVERSITY
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