Histogrammaßgleichung ist eine Methode in der Bildverarbeitung that enhances the contrast of an image by effectively spreading out the most frequent intensity values. This technique is particularly useful in enhancing the visibility of features in images that may be poorly contrasted due to lighting conditions or other factors.
The process of histogram equalization involves calculating the histogram of the image, which is a representation of the number of pixels for each intensity value. By transforming the pixel intensity values, the algorithm redistributes the intensity values so that they cover the entire range of possible values more uniformly. As a result, features that were previously difficult to discern become more visible.
Der Algorithmus funktioniert, indem er zuerst die kumulative Verteilungsfunktion (CDF) of the histogram. The CDF is then normalized to the range of pixel values, allowing for a mapping of the original pixel values to new values. This mapping effectively stretches the histogram, making it more uniform and enhancing the contrast of the image.
Histogrammaßgleichung wird in verschiedenen Anwendungen häufig verwendet, einschließlich medizinische Bildgebung, satellite imagery, and any field where image clarity is essential. However, it is important to note that this technique can sometimes lead to over-enhancement, introducing noise or artifacts into the image. To mitigate this, variations such as Adaptive Histogram Equalization (AHE) can be used, which applies the technique to smaller regions of the image to maintain local contrast while reducing the risk of global over-enhancement.