histogramnormalisering

Histogramnormalisering is a technique used in image processing and computer vision to adjust the contrast of an image by equalizing its intensity distribution. The goal is to spread out the most frequent intensity values, thereby increasing the global contrast of the image. This is achieved by calculating the cumulative distribution function (CDF) of the image's histogram and then mapping the original pixel intensities to new intensities based on this CDF.

The process typically involves counting the occurrences of each intensity level in the image, creating a histogram. Then, the cumulative distribution function is computed by summing the histogram values up to each intensity level. This CDF represents the proportion of pixels with intensities less than or equal to a given level. Finally, the image pixels are remapped to new intensity values using the CDF. This redistribution aims to make the intensity levels more evenly distributed across the entire range, enhancing details that might be lost in areas of low contrast.

Histogramnormalisering is particularly useful for images captured under varying lighting conditions or those with poor contrast. It can reveal hidden details in both dark and bright regions of an image. However, it's important to note that this method can sometimes amplify noise or introduce undesirable artifacts if applied inappropriately. Different variations of histogram equalization exist, such as adaptive histogram equalization (AHE) and contrast limited adaptive histogram equalization (CLAHE), which address some of the limitations of the basic method by applying the equalization locally rather than globally.