Adaptive Histogram Equalization
Adaptive Histogram Equalization (AHE) is an image processing technique used to enhance local contrast in images by applying histogram equalization to small regions rather than the entire image. It improves visibility of details in areas with varying lighting conditions, such as medical imaging or satellite photos, by adjusting pixel intensities based on the local neighborhood. This method helps reveal hidden features that might be obscured in standard histogram equalization.
Developers should learn AHE when working on computer vision, medical imaging, or remote sensing applications where local contrast enhancement is critical for analysis. It is particularly useful for tasks like tumor detection in MRI scans or feature extraction in aerial imagery, as it adapts to varying illumination across the image. Use it to preprocess images before applying machine learning models to improve accuracy in object recognition or segmentation.