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DCNN-HBA: Honey Badger Optimization and Deep Convolutional Neural Network Based a Novel Hybrid Model for Producing Quality Image

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Title DCNN-HBA: Honey Badger Optimization and Deep Convolutional Neural Network Based a Novel Hybrid Model for Producing Quality Image
 
Creator Niu, Sihan
Singh, Vineeta
Kumar, Alok
Verma, Deepak Kumar
Kumar, Sunil
Kaushik, Vandana Dixit
Chen, Zhiliang
Joshi, Kapil
 
Subject DCNN
Median filter
Medical imaging
Multi-focus image
Noise removal
 
Description 1304-1315
The processing of images is a major task in several domains like medical treatment, military, and surveillance. However,
the major reasons, like environmental criteria and technical issues made the imperative information tainted. The blurriness
represents degradations induced on the image that affected image contrast. There exist several techniques based on image
enhancement to improve image quality, but most of these techniques are complex to examine and impose image
degradation. An optimized deep technique is devised for producing quality pictures in which the input image is gathered
from the database. The pre-processing is done utilizing the median filter to discard the artefacts as well as the noise
accumulated in the images. The image enhancement is done with a Deep Convolutional Neural network (DCNN) and the
weight update in DCNN is carried out with the Honey Badger Optimization Algorithm (HBA). Thus, the DCNN-HBA helps
to enhance the quality of the image without any kind of degradation, like blurriness. The DCNN-HBA technique provides
better results with the highest mutual information (MI), highest universal quality index (UQI), maximum UQI, and enhanced
efficacy of image enhancement. The highest structural similarity index measurement (SSIM) is the maximum SSIM.
 
Date 2023-12-29T12:11:22Z
2023-12-29T12:11:22Z
2023-12
 
Type Article
 
Identifier 0022-4456 (Print); 0975-1084 (Online)
http://nopr.niscpr.res.in/handle/123456789/63131
https://doi.org/10.56042/jsir.v82i12.5132
 
Language en
 
Publisher NIScPR-CSIR, India
 
Source JSIR Vol.82(11) [November 2023]