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WeAbDeepCNN: Weighted Average Model and ASSCA based Two Level Fusion Scheme For Multi-Focus Images

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Title WeAbDeepCNN: Weighted Average Model and ASSCA based Two Level Fusion Scheme For Multi-Focus Images
 
Creator Singh, Vineeta
Kaushik, Vandana Dixit
 
Subject Atom search optimization
Deep convolutional neural network
Image fusion algorithm
Optimization technique
Multi-focus image fusion
 
Description 905-914
Fusion of images is a strategy that merges various moderately focused images or non-focused images of a single scene to
generate a fully focused, clear and sharp image. The goal of this research is to discover the focused regions and further
combination of focused regions of different source images into solitary image. However, there exist several issues in image
fusion that involves contrast reduction, block artifacts, and artificial edges. To solve this issue, a two level fusion scheme
has been devised, which involves weighted average model along with Atom Search Sine Cosine algorithm-based Deep
Convolutional Neural Network (ASSCA-based Deep CNN) and may be abbreviated as “WeAbDeepCNN” i.e. weighted
average model and ASSCA based Deep CNN. In the study two images are fed to initial fusion module, which is performed
using weighted average model. The fusion score are generated whose values are determined in an optimal manner. Thus,
final fusion is performed using proposed ASSCA-based Deep CNN. The Deep CNN training is carried out with proposed
ASSCA, which is devised by combining Sine Cosine Algorithm, abbreviated as SCA, as well as atom search optimization
(ASO). The proposed ASSCA-based Deep CNN offers improved performance in contrast to current state of the art
techniques with a highest value 1.52 of mutual information (MI), with a highest value of 32.55 dB of maximum Peak Signal
to Noise Ratio i.e. PSNR as well as value of 7.59 of Minimum Root Mean Square Error (RMSE).
 
Date 2021-10-05T11:43:01Z
2021-10-05T11:43:01Z
2021-10
 
Type Article
 
Identifier 0975-1084 (Online); 0022-4456 (Print)
http://nopr.niscair.res.in/handle/123456789/58227
 
Language en
 
Publisher NIScPR-CSIR, India
 
Source JSIR Vol.80(10) [October 2021]