Improving Image Sharpness by Surface Recognition

• Autorzy: Zerbino D.
• Języki publikacji: EN

Abstrakty

EN

The article proposes a rule for improving image sharpness and analyzes its implementation by means of the cellular automata formalism and neural networks. It has been proved, that the previously known contrasting algorithm, which uses a template and 3x3 pixels, can be improved considerably by repeatedly applying the iterative process over templates 2x2 with the rule "anti - blur" ( C 11 = C 11 x F - ( C 12 + C 21 + C 22) x S ) and gradient color correction at each step after the "anti - blur". Colors of images in the template are presented as real numbers (R, G, B). To correct the gradient (C11 < C12, C11 < C21, C11

Słowa kluczowe

EN

cellular automata; image contrasting; sharpness; correct gradient; logical correction of colors; neocognitron

• Wydawca: Polish Academy of Sciences, Branch in Lublin
• Czasopismo: ECONTECHMOD : An International Quarterly Journal on Economics of Technology and Modelling Processes
• Rocznik: 2019
• Tom: Vol. 8, No 2
• Strony: 39--44
• Opis fizyczny: Bibliogr. 14 poz., rys., wz.

Twórcy

autor

Zerbino D.

• Automated Control System Lviv Polytechnic National University S. Bandery 28a, 79008 Lviv, Ukraine; Lviv, Ukraine

Bibliografia

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Uwagi

Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019).