{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T06:43:59Z","timestamp":1740120239137,"version":"3.37.3"},"reference-count":36,"publisher":"World Scientific Pub Co Pte Ltd","issue":"12","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Patt. Recogn. Artif. Intell."],"published-print":{"date-parts":[[2020,11]]},"abstract":"<jats:p> 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. <\/jats:p>","DOI":"10.1142\/s0218001420520084","type":"journal-article","created":{"date-parts":[[2019,12,13]],"date-time":"2019-12-13T08:16:24Z","timestamp":1576224984000},"page":"2052008","source":"Crossref","is-referenced-by-count":2,"title":["Target Object Recognition Using Multiresolution SVD and Guided Filter with Convolutional Neural Network"],"prefix":"10.1142","volume":"34","author":[{"given":"Biswajit","family":"Biswas","sequence":"first","affiliation":[{"name":"Computer Science & Engineering, University of Calcutta, Kolkata, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9312-4189","authenticated-orcid":false,"given":"Swarup Kr","family":"Ghosh","sequence":"additional","affiliation":[{"name":"Computer Science & Engineering, Sister Nivedita University, Kolkata, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Anupam","family":"Ghosh","sequence":"additional","affiliation":[{"name":"Computer Science & Engineering, Netaji Subhash Engineering College, Kolkata, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chandan","family":"Chakraborty","sequence":"additional","affiliation":[{"name":"School of Medical Science & Technology, Indian Institute of Technology, Kharagpur, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pabitra","family":"Mitra","sequence":"additional","affiliation":[{"name":"Department of Computer Science & Engineering, Indian Institute of Technology, Kharagpur, India"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"219","published-online":{"date-parts":[[2020,3,23]]},"reference":[{"key":"S0218001420520084BIB001","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.artmed.2018.04.004","volume":"88","author":"Anna F.","year":"2018","journal-title":"Artif. 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