Applied program toolbox for contrasts' analysis of digital image

N.E. Andronova, O.V. Bondar', A.O. Borovkova, V.I. Borovkov, P.E. Grebenyuk, A.S. Gresserov, A.M. Chmutin, M.A. Chmutin


Consideration is given to digital images carrying significant information, localized below the threshold of human eye contrast perception and not always amenable to processing by means of modern software. The reason for such failures lies in the fact that, with rare exceptions, contrast enhancement is performed by controlling directly RGB coordinates of pixels. An alternative is the authorized systemic software for transforming perceptual contrasts. In the present paper two concepts – systemic operation and systematic organization of contrast enhancement toolbox structure – are differentiated. From the standpoint of contrast effect, the appropriate regular software was analyzed on the sufficiency of its functional content and considerable gaps among the required nomenclature of contrast enhancing functions were revealed. The advanced software set, on the contrary, is organized systematically – the potential maximum of contrast enhancing modes being achieved. Besides, mechanisms for perceptual contrasts' control are unified and combined with mechanisms for same-name color characteristics' control. In addition to such optimization, the titular contrast enhancing procedures are integrated with the accompanying procedures, which are carried out by stand-alone tools in the existing today software. A number of new contrast enhancement functions have been implemented. A set of contrast (and color) transformation programs is provided with tools for originals' analysis and diagnostics, for processed results' visualization. The process of the created set testing is documented. The operation of Ishihara's tests is presented in terms and categories of contrast theory. The reformatting of one of them into a triad of threshold tests is detailed. The created toolbox passed probation, during which method was founded and preliminary estimates were given for the thresholds of eye sensitivity to hue-contrast, to saturation-contrast and to brightness-contrast. An overview of possible applications of program tools' set for graphic contrasts' analysis is presented. Priority is given to the field of expert activities. In view of expert considerations, the perspective for toolbox development is outlined. Within such a framework the informative anchors are affixed to indicate directions of further work.

Full Text:

PDF (Russian)


Lindner A.J. Semantic Awareness for Automatic Image Interpretation. Thesis 5635. – Lausanne: ÉPFL, 2013. 137 p.

Antonov A.V. Sistemnyj analiz. – M.: Vysshaja shkola, 2004. 454 s.

Chmutin A.M. Contrast and Contrast Enhancement (in Logic of Visual Perception of Graphic Information). // International Journal of Open Information Technologies. 2022. V. 10. № 5. P. 44-52. [Jelektronnyj resurs]. – Rezhim dostupa:РВ.pdf (data obrashhenija: 01.03.2023).

Romanov V.N. Sistemnyj analiz dlja inzhenerov. – SPb.: SZTU, 2006. 186 s.

Chmutin A.M. Jekspertnoe issledovanie izobrazhenij na transporte. // Tehnicheskie jekspertizy na transporte. / Pod obshh. red. Ju.Ja. Komarova. – M.: Gorjachaja linija - Telekom, 2020. S. 195.

Chmutin A.M. Kontrast nasyshhennostej i jarkostnyj sdvig: paradoksy Photoshop. // International Journal of Open Information Technologies. 2019. V. 7. № 1. P. 12-24. [Jelektronnyj resurs]. – Rezhim dostupa: (data obrashhenija: 01.03.2023).

Starovoitov V.V., Golub Ju.I. Poluchenie i obrabotka izobrazhenij na JeVM. – Mn.: BNTU, 2018. 204 s.

Bezrjadin S.N., Burov P.A. Virirovanie izobrazhenij v programmnom obespechenii. v. 1.0. – San Francisco: KWE Int. Inc., 2006. 6 p. [Jelektronnyj resurs]. – Rezhim dostupa: (data obrashhenija: 14.05.2010).

Distante A., Distante C. Handbook of Image Processing and Computer Vision. Vol. 2 – Cham: Springer Nature, 2020. 431 p.

Ophthalmology. / Ed. by M. Yanoff and J.S. Duker. – Edinburgh: Elsevier, 2019. 1371 p.

Encyclopedia of Perception. / Ed. E.B. Goldstein. – Los Angeles: SAGE Publ., 2010. 1180 p.

Hunt R.W.G., Pointer M.R. Measuring Colour. – Chichester: Wiley, 2011. 469 p.

Ishihara S. Tests for Colour-Blindness. – Tokyo: Kanehara, 1993. 6 p. 30 pl.

Parfjonov P.S. Istorija i metodologija informatiki i vychislitel'noj tehniki – SPb.: SPbGU ITMO, 2010. 141 s.

Ablamejko S.V., Nedz'ved' A.M. Obrabotka opticheskih izobrazhenij kletochnyh struktur v medicine. – Mn.: OIPI NAN Belarusi, 2005. 156 s.

Zhuravel' I.M. Іnformacіjna tehnologіja avtomatizovanogo analіzu metalografіchnih ta fraktografіchnih zobrazhen': Dis. ... dokt. tehn. nauk. – L'viv, FMІ іm. G.V. Karpenka NAN Ukraїni, 2019. 321 s.

Narkevich A.N. Avtomatizirovannaja bakterioskopicheskaja diagnostika tuberkuljoza: Avtoref. diss. … doct. med. nauk. – Krasnojarsk: KGMU im. Vojno-Yaseneckogo, 2019. 47 s.

Schowengerdt R.A. Remote Sensing: Models and Methods for Image Processing. – Burlington: Elsevier, 2007. 515 p.

Krasnjashchih A.V. Obrabotka opticheskih izobrazhenij. – SPb.: NIU ITMO, 2012. 129 s.

Dalrymple B., Smith J. Forensic digital image processing optimization of impression evidence. – Boca Raton: CRC Press, 2018. 227 p.

Pach J.L., Krupa A., Antoniuk I. Text Area Detection in Handwritten Documents Scanned for Further Processing. // Machine Graphics & Vision. 2020. V. 29. № 1/4. P. 21-31. DOI: 10.22630/MGV.2020.29.1.2. [Jelektronnyj resurs]. – Rezhim dostupa: (data obrashhenija: 01.03.2023).


  • There are currently no refbacks.

Abava  Кибербезопасность FRUCT 2023

ISSN: 2307-8162