109649 20241220133820.0 doi 10.2991/IJCIS.D.210407.001 sideral 125504 ART-2021-125504 eng Neji, H. Universidad de Zaragoza (orcid)0000-0003-3595-005X Blur2sharp: A gan-based model for document image deblurring 2021 Access copy available to the general public Unrestricted The advances in mobile technology and portable cameras have facilitated enormously the acquisition of text images. However, the blur caused by camera shake or out-of-focus problems may affect the quality of acquired images and their use as input for optical character recognition (OCR) or other types of document processing. This work proposes an end-to-end model for document deblurring using cycle-consistent adversarial networks. The main novelty of this work is to achieve blind document deblurring, i.e., deblurring without knowledge of the blur kernel. Our method, named “Blur2Sharp CycleGAN, ” generates a sharp image from a blurry one and shows how cycle-consistent generative adversarial networks (CycleGAN) can be used in document deblurring. Using only a blurred image as input, we try to generate the sharp image. Thus, no information about the blur kernel is required. In the evaluation part, we use peak signal to noise ratio (PSNR) and structural similarity index (SSIM) to compare the deblurring images. The experiments demonstrate a clear improvement in visual quality with respect to the state-of-the-art using a dataset of text images. info:eu-repo/grantAgreement/ES/DGA/T59-20R info:eu-repo/semantics/openAccess by-nc http://creativecommons.org/licenses/by-nc/3.0/es/ 2.259 2021 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS 79 / 112 = 0.705 2021 Q3 T3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE 102 / 145 = 0.703 2021 Q3 T3 0.492 2021 Computer Science (miscellaneous) 2021 Q2 Computational Mathematics 2021 Q2 3.4 2021 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Halima, M.B. Hamdani, T.M. Nogueras-Iso, J. Universidad de Zaragoza (orcid)0000-0002-1279-0367 Alimi, A.M. 5007 570 Universidad de Zaragoza Dpto. Informát.Ingenie.Sistms. Área Lenguajes y Sistemas Inf. 14, 1 (2021), 1315-1321 Int. j. comput. intell. syst. International Journal of Computational Intelligence Systems 1875-6891 3088074 http://zaguan.unizar.es/record/109649/files/texto_completo.pdf Versión publicada 2671068 http://zaguan.unizar.es/record/109649/files/texto_completo.jpg?subformat=icon icon Versión publicada oai:zaguan.unizar.es:109649 articulos driver 2024-12-20-13:37:43 ARTICLE