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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.
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. There are two ways to think about and implement histogram equalization, either as image change or as palette change.
Changing the histogram to uniform distribution from an image is usually what we called histogram equalization. Figure 1 Figure 2 In discrete time, the area of gray level histogram is ∑ i = 0 k H ( p i ) {\displaystyle \sum _{i=0}^{k}H(p_{i})} (see figure 1) while the area of uniform distribution is ∑ i = 0 k G ( q i ) {\displaystyle \sum ...
The top row is a series of plots using the escape time algorithm for 10000, 1000 and 100 maximum iterations per pixel respectively. The bottom row uses the same maximum iteration values but utilizes the histogram coloring method. Notice how little the coloring changes per different maximum iteration counts for the histogram coloring method plots.
An example of histogram matching In image processing , histogram matching or histogram specification is the transformation of an image so that its histogram matches a specified histogram. [ 1 ] The well-known histogram equalization method is a special case in which the specified histogram is uniformly distributed .
A v-optimal histogram is based on the concept of minimizing a quantity which is called the weighted variance in this context. [1] This is defined as = =, where the histogram consists of J bins or buckets, n j is the number of items contained in the jth bin and where V j is the variance between the values associated with the items in the jth bin.
Otsu's method performs well when the histogram has a bimodal distribution with a deep and sharp valley between the two peaks. [ 6 ] Like all other global thresholding methods, Otsu's method performs badly in case of heavy noise, small objects size, inhomogeneous lighting and larger intra-class than inter-class variance. [ 7 ]
An image histogram is a type of histogram that acts as a graphical representation of the tonal distribution in a digital image. [1] It plots the number of pixels for each tonal value. By looking at the histogram for a specific image a viewer will be able to judge the entire tonal distribution at a glance.