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Histogram equalization is a method in image processing of contrast adjustment using the image's histogram. Histograms of an image before and after equalization.
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.
The waveform of a Gaussian white noise signal plotted on a graph. In signal processing, white noise is a random signal having equal intensity at different frequencies, giving it a constant power spectral density. [1]
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 .
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.
In image processing, the balanced histogram thresholding method (BHT), [1] is a very simple method used for automatic image thresholding.Like Otsu's Method [2] and the Iterative Selection Thresholding Method, [3] this is a histogram based thresholding method.
Darktable (stylized as darktable) is a free and open-source photography application and raw developer. Rather than being a raster graphics editor like Adobe Photoshop or GIMP, it comprises a subset of image editing operations specifically aimed at non-destructive raw image post-production.
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]