Suspect Multifocus Image Fusion Based on Sparse Denoising Autoencoder Neural Network for Police Multimodal Big Data Analysis

In recent years, the success rate of solving major criminal cases through big data has been greatly improved. The analysis of multimodal big data plays a key role in the detection of suspects. However, the traditional multiexposure image fusion methods have low efficiency and are largely time-consum...

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Bibliographic Details
Published in:Scientific programming Vol. 2021; pp. 1 - 12
Main Authors: Wang, Jin, Gao, Yanfei
Format: Journal Article
Language:English
Published: New York Hindawi 07.01.2021
John Wiley & Sons, Inc
Subjects:
Blurring
Computer vision
Crime
Data analysis
Deep learning
Defocusing
Design
Feature extraction
Image coding
Image processing
Image restoration
Methods
Neural networks
Noise reduction
Police
Principal components analysis
Target recognition
ISSN:1058-9244, 1875-919X
Online Access:Get full text
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Summary:In recent years, the success rate of solving major criminal cases through big data has been greatly improved. The analysis of multimodal big data plays a key role in the detection of suspects. However, the traditional multiexposure image fusion methods have low efficiency and are largely time-consuming due to the artifact effect in the image edge and other sensitive factors. Therefore, this paper focuses on the suspect multiexposure image fusion. The self-coding neural network based on deep learning has become a hotspot in the research of data dimension reduction, which can effectively eliminate the irrelevant and redundant learning data. In the case of limited field depth, due to the limited focusing depth of the camera, the focusing plane cannot obtain the global clear image of the target in the depth scene, which is prone to defocusing and blurring phenomena. Therefore, this paper proposes a multifocus image fusion based on a sparse denoising autoencoder neural network. To realize an unsupervised end-to-end fusion network, the sparse denoising autoencoder neural network is adopted to extract features and learn fusion rules and reconstruction rules simultaneously. The initial decision graph of the multifocus image is taken as a prior input to learn the rich detailed information of the image. The local strategy is added to the loss function to ensure that the image is restored accurately. The results show that this method is superior to the state-of-the-art fusion methods.
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ISSN:1058-9244
1875-919X
DOI:10.1155/2021/6614873