Histogram Equalization

The core concept of histogram equalization is to transform the grayscale histogram of an original image from a concentrated intensity range to a uniform distribution across the entire grayscale spectrum. This method performs non-linear stretching of the image, redistributing pixel values to achieve approximately equal pixel counts within certain grayscale ranges. Algorithm implementation generally involves computing the cumulative distribution function (CDF) of the image histogram and mapping original intensity values to new values using the normalized CDF as a transformation function.

However, histogram equalization has drawbacks. First, the number of grayscale levels may decrease after transformation, causing loss of certain details. Second, for images with high-peak histograms, the processed results may exhibit unnatural over-enhancement of contrast. Despite these limitations, histogram equalization remains widely used in image processing, particularly for enhancing local contrast when useful image data has similar contrast levels. The method enables better brightness distribution across the histogram, improving local contrast without affecting global contrast.

Histogram equalization is particularly useful for images with overly bright or dark backgrounds and foregrounds. It significantly improves bone structure visualization in X-ray images and enhances details in overexposed or underexposed photographs. A major advantage is its intuitive implementation using simple mathematical operations - typically achieved through histogram calculation, CDF computation, and intensity mapping functions. The process is reversible if the equalization function is known, requiring minimal computational overhead. However, a drawback is its non-selective data processing, which may increase background noise contrast while reducing useful signal contrast. Alternative methods like adaptive histogram equalization or region-specific enhancement techniques address this by applying different enhancement strategies based on image content characteristics.