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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.
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.
The optical transfer function (OTF) of an optical system such as a camera, microscope, human eye, or projector is a scale-dependent description of their imaging contrast. Its magnitude is the image contrast of the harmonic intensity pattern, + (), as a function of the spatial frequency, , while its complex argument indicates a phase shift ...
The following input grayscale image is to be changed to match the reference histogram. 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
The basic intent of the contrast enhancement technique is to adjust the local contrast in the image so as to bring out the clear regions or objects in the image . Low-contrast images often result from poor or non-uniform lighting conditions, a limited dynamic range of the imaging sensor , or improper settings of the lens aperture.
Contrast resolution or contrast-detail is an approach to describing the image quality in terms of both the image contrast and resolution. Contrast resolution is usually measured by generating a pattern from a test object that depicts how image contrast changes as the structures being imaged get smaller and closer together.
For example, attempting to read a pixel 3 units outside an edge reads one 3 units inside the edge instead. Crop / Avoid overlap Any pixel in the output image which would require values from beyond the edge is skipped. This method can result in the output image being slightly smaller, with the edges having been cropped.
Image registration or image alignment algorithms can be classified into intensity-based and feature-based. [3] One of the images is referred to as the moving or source and the others are referred to as the target, fixed or sensed images. Image registration involves spatially transforming the source/moving image(s) to align with the target image.