Search results
Results from the WOW.Com Content Network
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.
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.
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.
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. [2]
Histogram equalization is a popular example of these algorithms. Improvements in picture brightness and contrast can thus be obtained. In the field of computer vision, image histograms can be useful tools for thresholding. Because the information contained in the graph is a representation of pixel distribution as a function of tonal variation ...
The YIQ representation is sometimes employed in color image processing transformations. For example, applying a histogram equalization directly to the channels in an RGB image would alter the color balance of the image. Instead, the histogram equalization is applied to the Y channel of the YIQ or YUV representation of the image, which only ...
Histogram shape-based methods, where, for example, the peaks, valleys and curvatures of the smoothed histogram are analyzed. [3] Note that these methods, more than others, make certain assumptions about the image intensity probability distribution (i.e., the shape of the histogram),
Most threshold selection algorithms assume that the intensity histogram is multi-modal; typically bimodal. However, some types of images are essentially unimodal since a much larger proportion of just one class of pixels (e.g. the background) is present in the image, and dominates the histogram. In such circumstances many of the standard ...