Lightness Adaptation

Lightness adaptation, the process of adjusting images to account for varying illumination conditions, is crucial for improving image quality and enabling robust computer vision systems. Current research focuses on developing generalized algorithms, such as channel-selective normalization techniques, that can adapt to unseen lighting conditions, moving beyond methods trained only on specific lighting levels. This involves creating unified models capable of handling diverse light-related tasks like low-light enhancement and exposure correction, often drawing inspiration from biological visual systems. Improved lightness adaptation is vital for enhancing the performance of computer vision applications and improving the visual quality of images across diverse contexts.