histogramjustering

Histogram adjustment, sometimes referred to as histogram stretching or histogram equalization in image processing literature, is a technique used to modify the distribution of pixel intensities in an image. The goal is to improve contrast, reveal details, or prepare the image for further analysis by altering how frequently different brightness levels occur.

Global methods apply the same transformation to all pixels. Histogram equalization uses the image’s cumulative distribution function to redistribute intensities across the full dynamic range, often increasing overall contrast. Linear contrast stretching rescales the observed minimum and maximum values to the full range, while gamma correction applies a nonlinear power-law mapping to adjust midtones. Histogram specification or matching reshapes the output histogram to match a predefined target distribution, which helps standardize images from different sources.

Local or adaptive methods compute the transfer locally within small regions. Adaptive histogram equalization (AHE) and contrast-limited adaptive histogram equalization (CLAHE) enhance local contrast but can amplify noise if not carefully tuned. Local methods are useful for images with varying illumination but may introduce artifacts if the parameters are not chosen carefully.

Applications and considerations: histogram adjustment is used in photography, medical imaging, remote sensing, and computer vision to improve visibility and enable consistent analysis. Potential drawbacks include artifacts, color shifts when applied to all color channels, and noise amplification in homogeneous areas. Best practices include applying adjustments to luminance channels in color images, avoiding clipping, and validating the effect for downstream tasks.