Density-Based Histogram Partitioning and Local Equalization for Contrast Enhancement of Images | ||
| Journal of AI and Data Mining | ||
| مقاله 1، دوره 6، شماره 1، خرداد 2018، صفحه 1-12 اصل مقاله (2.35 M) | ||
| نوع مقاله: Original/Review Paper | ||
| شناسه دیجیتال (DOI): 10.22044/jadm.2017.910 | ||
| نویسندگان | ||
| M. Shakeri؛ M.H. Dezfoulian؛ H. Khotanlou* | ||
| Department of Computer Engineering, Bu-Ali Sina University, Hamedan, Iran. | ||
| چکیده | ||
| Histogram Equalization technique is one of the basic methods in image contrast enhancement. Using this method, in the case of images with uniform gray levels (with narrow histogram), causes loss of image detail and the natural look of the image. To overcome this problem and to have a better image contrast enhancement, a new two-step method was proposed. In the first step, the image histogram is partitioned into some sub-histograms according to mean value and standard deviation, which will be controlled with PSNR measure. In the second step, each sub-histogram will be improved separately and locally with traditional histogram equalization. Finally, all sub-histograms will be combined to obtain the enhanced image. Experimental results shows that this method would not only keep the visual details of the histogram, but also enhance image contrast. | ||
| کلیدواژهها | ||
| Contrast Enhancement؛ Histogram Modification؛ Image Quality Evaluation؛ Image Quality Enhancement | ||
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آمار تعداد مشاهده مقاله: 1,666 تعداد دریافت فایل اصل مقاله: 2,373 |
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