Illumination Enhancement

Illumination enhancement focuses on improving the quality of images and videos captured in low-light conditions, aiming to recover details and enhance overall brightness without introducing artifacts. Current research emphasizes the development of deep learning models, often employing transformer architectures and attention mechanisms, to achieve a balance between global brightness adjustment and preservation of fine details. These advancements are crucial for various applications, including autonomous driving, surveillance, and medical imaging, where clear visibility in low-light scenarios is essential for reliable performance and accurate analysis. The field is also exploring unsupervised learning techniques and integrated approaches that combine illumination enhancement with other image processing tasks like denoising and super-resolution for improved efficiency and quality.