Abstract
Image Enhancement is one of the important pre-processing part in any image processing system. It attempts to make Image/Video more understandable while keeping original information for the rest of an image-processing system. Histogram-based image enhancements divide histogram of the original image by one or more separating points and apply the conventional histogram equalization techniques on each sub-image. In this paper, a Genetic-Algorithm scheme tries to find the best point for separating the Histogram. The fitness function of the designed GA is chosen by an image quality measurement to preserve the information of the original image. The experimental results show the superiority of the proposed method than traditional histogram based image-enhancement techniques.
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Sedighi, S., Roopaei, M., Agaian, S. (2016). Genetic-Based Thresholds for Multi Histogram Equalization Image Enhancement. In: Perner, P. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2016. Lecture Notes in Computer Science(), vol 9729. Springer, Cham. https://doi.org/10.1007/978-3-319-41920-6_38
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DOI: https://doi.org/10.1007/978-3-319-41920-6_38
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