Image contrast enhancement based on histogram similarity

Many images suffer from poor contrast. It is necessary to enhance the contrast of images. Image enhancement improves an image appearance by increasing dominance of some features or by decreasing ambiguity between different regions of the image. Histogram equalization (HE) is widely used for contrast enhancement in a variety of applications due to its simple function and effectiveness. However, it usually results in excessive contrast enhancement, which causes the unnatural look and visual artifacts of the processed image. The improved algorithms, such as Brightness preserving Bi-Histogram Equalization, Recursive Mean-Separate Histogram Equalization, Dual Sub-Image Histogram Equalization and so on, are devoted to preserving the input image brightness and will be disappointed when the input image has low luminance. A novel method based on histogram similarity is developed to overcome the drawbacks of the classic HE algorithms for gray scale images. Compared to some of the conventional HE methods, the proposed method produces better contrast and image quality.