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dc.contributor.authorAteş, Hasan Fehmi
dc.contributor.authorYıldırım, Süleyman
dc.contributor.authorGüntürk, Bahadır Kürşat
dc.date.accessioned2023-07-10T10:09:17Z
dc.date.available2023-07-10T10:09:17Z
dc.date.issued2023en_US
dc.identifier.citationAteş, H. F., Yıldırım, S. ve Güntürk, B. K. (2023). Deep learning-based blind image super-resolution with iterative kernel reconstruction and noise estimation. Computer Vision and Image Understanding, 233. https://dx.doi.org/10.1016/j.cviu.2023.103718en_US
dc.identifier.issn1077-3142
dc.identifier.issn1090-235X
dc.identifier.urihttps://dx.doi.org/10.1016/j.cviu.2023.103718
dc.identifier.urihttps://hdl.handle.net/20.500.12511/11175
dc.description.abstractBlind single image super-resolution (SISR) is a challenging task in image processing due to the ill-posed nature of the inverse problem. Complex degradations present in real life images make it difficult to solve this problem using naive deep learning approaches, where models are often trained on synthetically generated image pairs. Most of the effort so far has been focused on solving the inverse problem under some constraints, such as for a limited space of blur kernels and/or assuming noise-free input images. Yet, there is a gap in the literature to provide a well-generalized deep learning-based solution that performs well on images with unknown and highly complex degradations. In this paper, we propose IKR-Net (Iterative Kernel Reconstruction Network) for blind SISR. In the proposed approach, kernel and noise estimation and high-resolution image reconstruction are carried out iteratively using dedicated deep models. The iterative refinement provides significant improvement in both the reconstructed image and the estimated blur kernel even for noisy inputs. IKR-Net provides a generalized solution that can handle any type of blur and level of noise in the input low-resolution image. IKR-Net achieves state-of-the-art results in blind SISR, especially for noisy images with motion blur.en_US
dc.language.isoengen_US
dc.publisherAcademic Press Inc.en_US
dc.rightsinfo:eu-repo/semantics/embargoedAccessen_US
dc.subjectSuper-Resolutionen_US
dc.subjectBlinden_US
dc.subjectIterativeen_US
dc.subjectDeep Networken_US
dc.titleDeep learning-based blind image super-resolution with iterative kernel reconstruction and noise estimationen_US
dc.typearticleen_US
dc.relation.ispartofComputer Vision and Image Understandingen_US
dc.departmentİstanbul Medipol Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Elektrik ve Elektronik Mühendisliği Bölümüen_US
dc.authorid0000-0003-0779-9620en_US
dc.identifier.volume233en_US
dc.relation.tubitakinfo:eu-repo/grantAgreement/TUBITAK/SOBAG/119E566
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.identifier.doi10.1016/j.cviu.2023.103718en_US
dc.institutionauthorGüntürk, Bahadır Kürşat
dc.identifier.wosqualityQ2en_US
dc.identifier.wos001010560700001en_US
dc.identifier.scopus2-s2.0-85162834377en_US
dc.identifier.scopusqualityQ1en_US


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