 Unsupervised document image binarization using a system of nonlinear partial differential equationsB.A. Jacobs and T. Celik Applied Mathematics and Computation, 2022, vol. 418, issue C Abstract: Partial differential equations have recently been established as a viable framework for image processing, particularly for image binarization. One drawback of this framework is the requirement for manual parameter tuning. In this work we propose a novel development wherein the spatio-temporal dynamics of the thresholding parameter are governed by an additional partial differential equation which is engineered to exhibit desirable traits. While the model can still be tuned manually to achieve optimal results, we show experimentally that the present framework is near optimal for the default choice of parameter, τ. This novel system enforces a smooth evolution of the threshold map while still offering locally adaptive thresholding properties, a requirement for non-uniformly illuminated images. The proposed model is applied to images through a rudimentary finite difference based numerical method due to the parallelizability and provable stability of the method. The proposed work offers an unsupervised binarization scheme and is benchmarked against state-of-the-art methods in the field. Keywords: Image Processing; Fitzhugh-Nagumo; Finite Difference; Partial Differential Equations (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:apmaco:v:418:y:2022:i:c:s0096300321008882 DOI: 10.1016/j.amc.2021.126806 Access Statistics for this article Applied Mathematics and Computation is currently edited by Theodore Simos More articles in Applied Mathematics and Computation from Elsevier |
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