Contrast enhancement for mimimum mean brightness error from histogram partitioning

This paper presents the image enhancing using a mean separated histogram equalization method. To provide the minimum mean brightness error after the histogram modification. It separates the input image's histogram into n (n=1,2,3,⋯) groups based on input mean before equalizing them independently. The image initially is separated class by calculated threshold level and each class is histogram equalized to entire image, and gets lowest AMBE (AMBE: Absolute Mean Brightness Error). The result found that AMBE gradually reduces when the separation is increased. Therefore, the error threshold is assigned in order to automatically dividing the original histogram for obtaining the desired AMBE. This process will be applied to remote sensing data by treating each region of histogram independently. Also Tenengrad is employed in order to verify the contrast performance. The image performance is considered higher if its Tenengrad value is larger.