Biological Image Processing Algorithm Based on Attention Mechanism and Convolutional Neural Network

This paper proposes a model algorithm based on convolutional neural network combined with attention mechanism to realize fast and accurate identification of biological image. Firstly, deformable convolution is used to extract features in the horizontal and vertical directions, respectively. Secondly...

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Main Author: Lisong Ou
Format: Article
Language:English
Published: Wiley 2023-01-01
Series:Advances in Multimedia
Online Access:http://dx.doi.org/10.1155/2023/7464628
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author Lisong Ou
author_facet Lisong Ou
author_sort Lisong Ou
collection DOAJ
description This paper proposes a model algorithm based on convolutional neural network combined with attention mechanism to realize fast and accurate identification of biological image. Firstly, deformable convolution is used to extract features in the horizontal and vertical directions, respectively. Secondly, attention modules are used to capture remote dependencies in one spatial direction, while accurate position information is retained in another spatial direction, so that information in both vertical and horizontal directions can be retained; after a series of transformations, the attention vector is obtained and multiplied back to the original feature vector as a weight factor. The experimental results show that the proposed algorithm can effectively improve the image quality, improve the image clarity, avoid color distortion, and achieve good results in both synthetic and real low-illumination images, and the subjective and objective evaluation indicators are better than the contrast algorithm.
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publishDate 2023-01-01
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series Advances in Multimedia
spelling doaj-art-f648bcf9b4e64f9a940e24bdc0bb1eae2025-08-20T02:23:00ZengWileyAdvances in Multimedia1687-56992023-01-01202310.1155/2023/7464628Biological Image Processing Algorithm Based on Attention Mechanism and Convolutional Neural NetworkLisong Ou0College of ScienceThis paper proposes a model algorithm based on convolutional neural network combined with attention mechanism to realize fast and accurate identification of biological image. Firstly, deformable convolution is used to extract features in the horizontal and vertical directions, respectively. Secondly, attention modules are used to capture remote dependencies in one spatial direction, while accurate position information is retained in another spatial direction, so that information in both vertical and horizontal directions can be retained; after a series of transformations, the attention vector is obtained and multiplied back to the original feature vector as a weight factor. The experimental results show that the proposed algorithm can effectively improve the image quality, improve the image clarity, avoid color distortion, and achieve good results in both synthetic and real low-illumination images, and the subjective and objective evaluation indicators are better than the contrast algorithm.http://dx.doi.org/10.1155/2023/7464628
spellingShingle Lisong Ou
Biological Image Processing Algorithm Based on Attention Mechanism and Convolutional Neural Network
Advances in Multimedia
title Biological Image Processing Algorithm Based on Attention Mechanism and Convolutional Neural Network
title_full Biological Image Processing Algorithm Based on Attention Mechanism and Convolutional Neural Network
title_fullStr Biological Image Processing Algorithm Based on Attention Mechanism and Convolutional Neural Network
title_full_unstemmed Biological Image Processing Algorithm Based on Attention Mechanism and Convolutional Neural Network
title_short Biological Image Processing Algorithm Based on Attention Mechanism and Convolutional Neural Network
title_sort biological image processing algorithm based on attention mechanism and convolutional neural network
url http://dx.doi.org/10.1155/2023/7464628
work_keys_str_mv AT lisongou biologicalimageprocessingalgorithmbasedonattentionmechanismandconvolutionalneuralnetwork