Image Contrast Enhancement Using Adaptive Inverse Hyperbolic Tangent Algorithm Based on Image Segmentation by Watershed Technology

The drawback of using a global operator is its inability in revealing image details of local luminance variation. On the contrary, the advantage of a local operator is its capability of revealing the details of luminance level information in an image. In the image enhancement technology, we have proposed local contrast enhancement based on Adaptive Inverse Hyperbolic Tangent Algorithm (AIHT), and, further, the image contrast enhancement algorithms can be adaptively adjusted according to the characteristics of the image. Its image segmentation method is based on “row” or “column” scale oriented. Such benefits can be for different directions of the light source, and select more suitable processing methods to resolve overexposed and underexposed phenomenon. The regional image segmentation to section parameters was adjusted for the region, so that image contrast enhancement can effectively improve the shortcomings of global contrast enhancement. However, the image segmentation method is section parameters to use rows for the selected region size, and bring about unevenly distributed brightness values in the region. As a result, image enhancement will be affected. Therefore, this paper proposes image segmentation by watershed technology, which base brightness values for the selected region size. That result evenly distributes brightness values in the region, which can be applied to the image contrast enhancement process in order to achieve a clearer display quality and contrast enhancement. We also use Adaptive Removing Glare Algorithm to perform noise filter for the original image. That is the pre-processing for image contrast enhancement to achieve a more favorable image enhancement. Experimental results show that the proposed algorithm exhibits the ability to enhance local details while being powerful, and that can take shape using local detail and edge information.