Paper Info
Title
Target Object Recognition Using Multiresolution Svd And Guided Filter With Convolutional Neural Network
Abstract
To design an efficient fusion scheme for the generation of a highly informative fused image by combining multiple images is still a challenging task in computer vision. A fast and effective image fusion scheme based on multi-resolution singular value decomposition (MR-SVD) with guided filter (GF) has been introduced in this paper. The proposed scheme decomposes an image of two-scale by MR-SVD into a lower approximate layer and a detailed layer containing the lower and higher variations of pixel intensity. It generates lower and details of left focused (LF) and right focused (RF) layers by applying the MR-SVD on each series of multi-focus images. GF is utilized to create a refined and smooth-textured weight fusion map by the weighted average approach on spatial features of the lower and detail layers of each image. A fused image of LF and RF has been achieved by the inverse MR-SVD. Finally, a deep convolutional autoencoder (CAE) has been applied to segment the fused results by generating the trained-patches mechanism. Comparing the results by state-of-the-art fusion and segmentation methods, we have illustrated that the proposed schemes provide superior fused and its segment results in terms of both qualitatively and quantitatively.
Year
DOI
Venue
2020
10.1142/S0218001420520084
INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE
Keywords
DocType
Volume
Image fusion, image segmentation, convolutional neural network, autoencoder, multi-resolution SVD, guided filter
Journal
34
Issue
ISSN
Citations 
12
0218-0014
0
PageRank 
References 
Authors
0.34
0
5
Name
Order
Citations
PageRank
Biswajit Biswas154.82
Swarup Kr Ghosh200.34
Anupam Ghosh3368.10
Chandan Chakraborty453750.60
Pabitra Mitra51729126.79