Deep learning-based blind image super-resolution using iterative networks

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dc.authorid0000-0002-6842-1528
dc.authorid0000-0003-0779-9620
dc.contributor.authorYaar, Asfand
dc.contributor.authorAteş, Hasan Fehmi
dc.contributor.authorGüntürk, Bahadır Kürşat
dc.date.accessioned2022-03-08T06:42:11Z
dc.date.available2022-03-08T06:42:11Z
dc.date.issued2021
dc.departmentİstanbul Medipol Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümü
dc.departmentİstanbul Medipol Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Elektrik ve Elektronik Mühendisliği Bölümü
dc.description.abstractDeep learning-based single image super-resolution (SR) consistently shows superior performance compared to the traditional SR methods. However, most of these methods assume that the blur kernel used to generate the low-resolution (LR) image is known and fixed (e.g. bicubic). Since blur kernels involved in real-life scenarios are complex and unknown, per-formance of these SR methods is greatly reduced for real blurry images. Reconstruction of high-resolution (HR) images from randomly blurred and noisy LR images remains a challenging task. Typical blind SR approaches involve two sequential stages: i) kernel estimation; ii) SR image reconstruction based on estimated kernel. However, due to the ill-posed nature of this problem, an iterative refinement could be beneficial for both kernel and SR image estimate. With this observation, in this paper, we propose an image SR method based on deep learning with iterative kernel estimation and image reconstruction. Simulation results show that the proposed method outperforms state-of-the-art in blind image SR and produces visually superior results as well.
dc.identifier.citationYaar, A., Ateş, H. F. ve Güntürk, B. K. (2021). Deep learning-based blind image super-resolution using iterative networks. 2021 International Conference on Visual Communications and Image Processing, VCIP. Munich, 5-8 December 2021. https://doi.org/10.1109/VCIP53242.2021.9675367
dc.identifier.doi10.1109/VCIP53242.2021.9675367
dc.identifier.isbn9781728185514
dc.identifier.scopus2-s2.0-85125222150
dc.identifier.scopusqualityN/A
dc.identifier.urihttps://doi.org/10.1109/VCIP53242.2021.9675367
dc.identifier.urihttps://hdl.handle.net/20.500.12511/9103
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorYaar, Asfand
dc.institutionauthorAteş, Hasan Fehmi
dc.institutionauthorGüntürk, Bahadır Kürşat
dc.language.isoen
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.relation.ispartof2021 International Conference on Visual Communications and Image Processing, VCIPen_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/embargoedAccess
dc.subjectBlind
dc.subjectDeep Learning
dc.subjectIterative
dc.subjectKernel Estimation
dc.subjectSuper-Resolution
dc.titleDeep learning-based blind image super-resolution using iterative networks
dc.typeConference Object

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