Meanshift Segmentation Guided Spatially Adaptive Histogram Equalization

We propose a meanshift segmentation guided local histogram equalization method which enhances the visual quality of a single low contrast image. The meanshift segmentation partitions the image into local regions according to the spatial and intensity distances between pixels. After the image is partitioned into local regions, the local range of intensity and the number of pixels in the local region are taken into account to compute the new local range of intensity. The local contrast is well enhanced with the proposed method even in regions which have small histogram components, for which the global histogram equalization fails to obtain an enhanced contrast.