efd8ceaa78d3dade295e678976ad81ed pmc_8953585.pdf 2d7f7298180dc51460643aa8aaf34de36becdaf3 pmc_8953585.pdf e547e4603055b6e2957b61ad490bac95a2e92eb7b620e8a94c304970c160bd96 pmc_8953585.pdf Title: A Novel Domain Transfer-Based Approach for Unsupervised Thermal Image Super-Resolution Subject: This paper presents a transfer domain strategy to tackle the limitations of low-resolution thermal sensors and generate higher-resolution images of reasonable quality. The proposed technique employs a CycleGAN architecture and uses a ResNet as an encoder in the generator along with an attention module and a novel loss function. The network is trained on a multi-resolution thermal image dataset acquired with three different thermal sensors. Results report better performance benchmarking results on the 2nd CVPR-PBVS-2021 thermal image super-resolution challenge than state-of-the-art methods. The code of this work is available online. Keywords: thermal image super-resolution; unsupervised super-resolution; thermal images; attention module; semiregistered thermal images Author: Rafael E. Rivadeneira, Angel D. Sappa, Boris X. Vintimilla and Riad Hammoud Creator: LaTeX with hyperref Producer: pdfTeX-1.40.21 CreationDate: Mon Mar 14 16:00:01 2022 CET ModDate: Mon Mar 14 16:02:57 2022 CET Tagged: no UserProperties: no Suspects: no Form: none JavaScript: no Pages: 14 Encrypted: no Page size: 595.276 x 841.89 pts (A4) Page rot: 0 File size: 1130713 bytes Optimized: no PDF version: 1.7 name type encoding emb sub uni object ID ------------------------------------ ----------------- ---------------- --- --- --- --------- QMMPXG+URWPalladioL-Ital Type 1 Custom yes yes no 10 0 PIIYPA+URWPalladioL-Bold Type 1 Custom yes yes no 15 0 EYEFPA+URWPalladioL-Roma Type 1 Custom yes yes no 19 0 RVSEMT+TimesNewRomanPS-BoldItalicMT Type 1C WinAnsi yes yes no 27 0 MCBCCI+SourceSansRoman-Regular Type 1C Custom yes yes no 49 0 MCBCCM+SourceSansRoman.613wght Type 1C Custom yes yes no 53 0 MCBCCO+SourceSansRoman-Bold Type 1C Custom yes yes no 57 0 TSPQHK+CMSY10 Type 1 Builtin yes yes no 69 0 ZPGVCH+CMEX10 Type 1 Builtin yes yes no 82 0 GIGFZE+CMR10 Type 1 Builtin yes yes no 86 0 BAAAAA+Arial-BoldMT CID TrueType Identity-H yes yes yes 102 0 AAAAAA+ArialMT CID TrueType Identity-H yes yes yes 107 0 QQXUPP+PazoMath Type 1 Builtin yes yes no 115 0 ArialMT CID TrueType Identity-H yes no yes 124 0 FKPFBV+PazoMath-Italic Type 1 Builtin yes yes no 132 0 Jhove (Rel. 1.22.1, 2019-04-17) Date: 2023-05-18 02:28:44 CEST RepresentationInformation: pmc_8953585.pdf ReportingModule: PDF-hul, Rel. 1.12.1 (2019-04-17) LastModified: 2022-04-26 17:59:52 CEST Size: 1130713 Format: PDF Version: 1.7 Status: Well-Formed and valid SignatureMatches: PDF-hul MIMEtype: application/pdf PDFMetadata: Objects: 322 FreeObjects: 1 IncrementalUpdates: 0 DocumentCatalog: PageLayout: SinglePage PageMode: UseNone Outlines: Item: Title: Introduction Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@12f41634 Item: Title: Related Work Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@13c27452 Children: Item: Title: Benchmark Datasets Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@262b2c86 Item: Title: Super-Resolution Approaches Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@371a67ec Item: Title: Proposed Approach Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@5ed828d Children: Item: Title: Architecture Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@50d0686 Item: Title: Datasets Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@7a3d45bd Item: Title: Evaluation Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@1e7c7811 Item: Title: Experimental Results Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@77ec78b9 Children: Item: Title: Settings Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@1a3869f4 Item: Title: Results Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@a38d7a3 Item: Title: Conclusions Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@77f99a05 Item: Title: References Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@63440df3 Info: Title: A Novel Domain Transfer-Based Approach for Unsupervised Thermal Image Super-Resolution Author: Rafael E. Rivadeneira, Angel D. Sappa, Boris X. Vintimilla and Riad Hammoud Subject: This paper presents a transfer domain strategy to tackle the limitations of low-resolution thermal sensors and generate higher-resolution images of reasonable quality. The proposed technique employs a CycleGAN architecture and uses a ResNet as an encoder in the generator along with an attention module and a novel loss function. The network is trained on a multi-resolution thermal image dataset acquired with three different thermal sensors. Results report better performance benchmarking results on the 2nd CVPR-PBVS-2021 thermal image super-resolution challenge than state-of-the-art methods. The code of this work is available online. 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