Image denoising method based on attention and dense connection residual block convolution kernel neural network

A convolutional neural network, densely connected technology, applied in the field of attention and densely connected residual block convolutional neural network image denoising, which can solve problems such as time-consuming and cost-intensive, complex optimization, etc.

Pending Publication Date: 2021-11-05
HENAN UNIVERSITY
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Problems solved by technology

Traditional image denoising methods, the more common ones include three-dimensional filtering based on non-local block matching (BM3D), weighted kernel norm minimization (WNNM) denoising algorithms, although t

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  • Image denoising method based on attention and dense connection residual block convolution kernel neural network
  • Image denoising method based on attention and dense connection residual block convolution kernel neural network
  • Image denoising method based on attention and dense connection residual block convolution kernel neural network

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Embodiment Construction

[0048] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the present invention. Apparently, the described embodiment is only a part of the implementation of the present invention, rather than the entire implementation. Based on the embodiment of the present invention, all other embodiments obtained by those skilled in the art without creative work belong to The protection scope of the present invention.

[0049] like figure 1 As shown, an attention and densely connected residual block convolutional neural network image denoising method includes the following steps:

[0050] Step S1: constructing a training data set, and performing a preprocessing operation on the training data set;

[0051] Step S2: Construct a network denoising model using a convolutional neural network combining an attention mechanism and a densely connected residual block;

[0052] Step S3:...

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Abstract

The invention discloses an image denoising method based on an attention and dense connection residual block convolutional neural network. The method comprises the following steps: constructing a training data set, and performing an preprocessing operation on the training data set; constructing a network denoising model by using a convolutional neural network combining an attention mechanism and a dense connection residual block; setting a hyper-parameter and a loss function of the network denoising model, and optimizing the loss function; selecting images of different noise levels in the training data set, and training the network denoising model to obtain a trained network model; and performing image denoising according to the trained network model, and evaluating the noise images by using peak signal-to-noise ratio indexes. The method has the beneficial effects of improving the denoising performance and the imaging quality.

Description

technical field [0001] The invention belongs to the field of computer vision and image processing, in particular to an image denoising method of attention and densely connected residual block convolutional neural network. Background technique [0002] Images are often disturbed by many adverse factors during the process of acquisition, storage, recording and transmission, which degrades, distorts and degrades the quality to a certain extent, resulting in the existence of noise in the acquired images, thus affecting the quality of the images. Therefore, in order to obtain high-quality digital images and restore the information of the original image from the noisy image, it is necessary to perform noise reduction processing on the image to maintain the integrity of the original information as much as possible while removing useless information in the signal. for subsequent applications. [0003] Image denoising is a classic problem in the field of image processing, and it is ...

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Application Information

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IPC IPC(8): G06T5/00G06N3/04G06N3/08
CPCG06T5/002G06N3/08G06T2207/20081G06T2207/20084G06N3/045
Inventor 宋亚林李小艳孙琪
Owner HENAN UNIVERSITY
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