Histogram Equalization

Histogram equalization is an image processing technique aiming to improve contrast by redistributing the intensity levels of an image, making details more visible. Current research focuses on adapting and extending histogram equalization for specific applications, such as enhancing low-light images, improving the performance of deep learning models for image classification and segmentation (often using architectures like U-Net and Swin Transformer), and mitigating artifacts in various imaging modalities (e.g., SAR, WCE). These advancements have significant implications for diverse fields, including medical imaging, remote sensing, and computer vision, by improving image quality and enabling more accurate analysis and interpretation.