Blur2sharp: A gan-based model for document image deblurring
Neji, H. ; Halima, M.B. ; Hamdani, T.M. ; Nogueras-Iso, J. (Universidad de Zaragoza) ; Alimi, A.M.
Resumen: 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.
Idioma: Inglés
DOI: 10.2991/IJCIS.D.210407.001
Año: 2021
Publicado en: International Journal of Computational Intelligence Systems 14, 1 (2021), 1315-1321
ISSN: 1875-6891
Factor impacto JCR: 2.259 (2021)
Categ. JCR: COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS rank: 79 / 112 = 0.705 (2021) - Q3 - T3
Categ. JCR: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE rank: 102 / 146 = 0.699 (2021) - Q3 - T3
Factor impacto CITESCORE: 3.4 - Mathematics (Q1) - Computer Science (Q2)
Factor impacto SCIMAGO: 0.492 - Computer Science (miscellaneous) (Q2) - Computational Mathematics (Q2)
Financiación: info:eu-repo/grantAgreement/ES/DGA/T59-20R
Tipo y forma: Article (Published version)
Área (Departamento): Área Lenguajes y Sistemas Inf. (Dpto. Informát.Ingenie.Sistms.)
You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use. You may not use the material for commercial purposes.
Exportado de SIDERAL (2023-05-18-14:10:44)
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Idioma: Inglés
DOI: 10.2991/IJCIS.D.210407.001
Año: 2021
Publicado en: International Journal of Computational Intelligence Systems 14, 1 (2021), 1315-1321
ISSN: 1875-6891
Factor impacto JCR: 2.259 (2021)
Categ. JCR: COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS rank: 79 / 112 = 0.705 (2021) - Q3 - T3
Categ. JCR: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE rank: 102 / 146 = 0.699 (2021) - Q3 - T3
Factor impacto CITESCORE: 3.4 - Mathematics (Q1) - Computer Science (Q2)
Factor impacto SCIMAGO: 0.492 - Computer Science (miscellaneous) (Q2) - Computational Mathematics (Q2)
Financiación: info:eu-repo/grantAgreement/ES/DGA/T59-20R
Tipo y forma: Article (Published version)
Área (Departamento): Área Lenguajes y Sistemas Inf. (Dpto. Informát.Ingenie.Sistms.)
You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use. You may not use the material for commercial purposes. Exportado de SIDERAL (2023-05-18-14:10:44)
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Record created 2022-02-09, last modified 2023-05-19