Zerbino, D.2020-02-282020-02-282019-06-262019-06-26Zerbino D. Improving image sharpness by surface recognition / D. Zerbino // Econtechmod : scientific journal. — Lublin, 2019. — Vol 8. — No 4. — P. 39–44.https://ena.lpnu.ua/handle/ntb/46304The 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 <C 22, C 12 < C 22, C 21 < C 22) it is necessary to choose a number Cij, that requires minimal tightening in the direction of the neighbor's color. Number of necessary iterations of the rules application depends on the image.39-44encellular automataimage contrastingsharpnesscorrect gradientlogical correction of colorsneocognitronArticle© Copyright by Lviv Polytechnic National University 2019© Copyright by University of Engineering and Economics in Rzeszów 20196Zerbino D. Improving image sharpness by surface recognition / D. Zerbino // Econtechmod : scientific journal. — Lublin, 2019. — Vol 8. — No 4. — P. 39–44.