Image denoising method based on local density loss

A local density and image technology, applied in the field of neural networks, can solve problems such as gradient explosion or dispersion, deep network structure, etc., and achieve good performance results

Active Publication Date: 2021-07-23
FUZHOU UNIV
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  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

However, these methods have deep network structures, which are prone to gradient explosion or dispersion problems during training.

Method used

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  • Image denoising method based on local density loss
  • Image denoising method based on local density loss
  • Image denoising method based on local density loss

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[0046] In order to make the features and advantages of this patent more obvious and easy to understand, the following special examples are described in detail as follows:

[0047] It should be pointed out that the following detailed description is exemplary and intended to provide further explanation to the present application. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.

[0048]It should be noted that the terminology used here is only for describing specific implementations, and is not intended to limit the exemplary implementations according to the present application. As used herein, unless the context clearly dictates otherwise, the singular is intended to include the plural, and it should also be understood that when the terms "comprising" and / or "comprising" are used in this specification, they mean There are features, steps, o...

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Abstract

The invention provides an image denoising method based on local density loss, which is characterized by comprising the following steps of: firstly, performing matrix completion on a matrix with missing image pixel values by adopting a full-connection neural network model; then, parameters are updated through back propagation and gradient descent, derivation is carried out on the Gaussian influence function to obtain a Gaussian density loss function, and the Gaussian density loss function is used for measuring whether model prediction is good or bad; according to the method, matrix completion can be effectively completed, the local density loss function is introduced to measure the model prediction quality, and compared with other methods, the method has better performance in the image denoising task and has higher practical value.

Description

technical field [0001] The invention belongs to the technical fields of neural network, matrix completion and image denoising, and in particular relates to an image denoising method based on local density loss. Background technique [0002] The study of image denoising is an important part of the field of computer vision. In recent years, deep learning-based denoising methods have been successfully applied to synthetic noise, but their generalization performance to real noise is poor. Real noise refers to the noise that exists in images captured by camera equipment under poor lighting conditions, camera shake, object movement, spatial pixel misalignment, color brightness mismatch, etc. It has unknown noise levels, various types of noise, and complex noise distribution. and difficult to parameterize. Synthetic noise means that the noise type conforms to a certain probability distribution, and the noise level can be set independently, such as Gaussian noise, salt and pepper ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T5/00G06K9/40G06K9/46G06N3/04G06N3/08
CPCG06T5/002G06N3/084G06T2207/10024G06T2207/20081G06V10/30G06V10/56G06N3/045
Inventor 王石平方惠王允斌陈昭炯
Owner FUZHOU UNIV
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