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Adaptive histogram equalization (AHE) is a computer image processing technique used to improve contrast in images. It differs from ordinary histogram equalization in the respect that the adaptive method computes several histograms, each corresponding to a distinct section of the image, and uses them to redistribute the lightness values of the image.
According to statistics on Open Hub, [31] there is a total of 95 contributors and almost 402,000 lines of code (this include many libraries upon which OTB is built). OTB in also use for the development of the operational ground segment for the VENμS (Vegetation and Environment monitoring on a New Micro Satellite) and the ESA Sentinel-2 missions.
For example, if applied to 8-bit image displayed with 8-bit gray-scale palette it will further reduce color depth (number of unique shades of gray) of the image. Histogram equalization will work the best when applied to images with much higher color depth than palette size, like continuous data or 16-bit gray-scale images.
A major drawback to application of the algorithm is an inherent reduction in overall image contrast produced by the operation. [1] When utilized for image enhancement, the difference of Gaussians algorithm is typically applied when the size ratio of kernel (2) to kernel (1) is 4:1 or 5:1.
AIPS++ provides facilities for calibration, editing, image formation, image enhancement, and analysis of images and other astronomical data. A major focus is on reduction of data from both single-dish and aperture synthesis radio telescopes. Although the tools provided in AIPS++ are mainly designed for processing data from varieties of radio ...
The contrast enhancement tries to change the intensity of the pixel in the image, particularly in the input image for the purpose to obtain a more enhanced image .It is based on the number of techniques namely local, global, dark and bright levels of contrast .The contrast enhancement is considered as the amount of color or gray differentiation ...
The improvement to image quality is often attractive, since the same validation issues are present even for un-enhanced images. For deconvolution to be effective, all variables in the image scene and capturing device need to be modeled, including aperture , focal length , distance to subject, lens, and media refractive indices and geometries.
The input image has the following histogram Histogram of input image. It will be matched to this reference histogram to emphasize the lower gray levels. Desired reference histogram. After matching, the output image has the following histogram. Histogram of output image after matching. And looks like this. Output image after histogram matching