Abstract
Creating new, effective, and easy-to-implement techniques of image enhancement for real-time use in mobile applications is now extremely relevant. This paper addresses the problem of improving the efficiency of enhancing images by transforming their intensity in automatic mode. The purpose of this work is to improve the efficiency of enhancing the images by using the technique of piecewise linear contrast stretching. To this end, two new approaches to defining the gain factors have been proposed to implement the technique of piecewise linear stretching for the case of an arbitrary finite number of intervals. The first approach is based on the assumption that mean-separated intervals should be stretched to the same size (length) in the processed image. Another proposed approach is based on the analysis of the number and cumulative brightness of elements in the mean-separated intervals. The proposed approaches to defining gain factors ensure the implementation of the procedure of piecewise linear stretching for any selected number of intervals. To demonstrate the capabilities of these approaches, a new technique of recursive mean-separate contrast stretching (RMSCS) was proposed, which is based on the proposed methods of defining gain factors. The RMSCS technique provides a more uniform distribution of the contrast of objects in the image compared to traditional piecewise-linear contrast stretching. The proposed RMSCS technique has a number of advantages over known methods of transforming intensity and can be considered as an alternative to the widely used technique of histogram equalization and its modifications, in particular, based on the sub-histograms equalization.
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Yelmanov, S., Romanyshyn, Y. (2021). New Technique of Recursive Mean-Separate Contrast Stretching for Image Enhancement. In: Shakhovska, N., Medykovskyy, M.O. (eds) Advances in Intelligent Systems and Computing V. CSIT 2020. Advances in Intelligent Systems and Computing, vol 1293. Springer, Cham. https://doi.org/10.1007/978-3-030-63270-0_73
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