![]() In scientific imaging where spatial correlation is more important than intensity of signal (such as separating DNA fragments of quantized length), the small signal-to-noise ratio usually hampers visual detections. It may increase the contrast of background noise, while decreasing the usable signal. ![]() A disadvantage of the method is that it is indiscriminate. The calculation is not computationally intensive. So in theory, if the histogram equalization function is known, then the original histogram can be recovered. ![]() A key advantage of the method is that it is a fairly straightforward technique adaptive to the input image and an invertible operator. In particular, the method can lead to better views of bone structure in x-ray images, and to better detail in photographs that are either over or under-exposed. The method is useful in images with backgrounds and foregrounds that are both bright or both dark. ![]() Histogram equalization accomplishes this by effectively spreading out the highly populated intensity values which are used to degrade image contrast. This allows for areas of lower local contrast to gain a higher contrast. Through this adjustment, the intensities can be better distributed on the histogram utilizing the full range of intensities evenly. ![]() This method usually increases the global contrast of many images, especially when the image is represented by a narrow range of intensity values. Histograms of an image before and after equalization. Histogram equalization is a method in image processing of contrast adjustment using the image's histogram. Please consider expanding the lead to provide an accessible overview of all important aspects of the article. This article's lead section may be too short to adequately summarize the key points. ![]()
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