Illumination Correction

Illumination correction aims to improve image quality by mitigating the adverse effects of uneven or insufficient lighting. Current research focuses on developing sophisticated deep learning models, including diffusion transformers and convolutional neural networks, often incorporating techniques like Retinex-based processing and contrastive learning to achieve robust and efficient illumination adjustments across diverse image types and conditions. These advancements are crucial for improving the accuracy of various computer vision tasks, such as object detection, medical image analysis, and autonomous driving, where consistent and reliable illumination is essential for optimal performance. Furthermore, research is exploring unsupervised and unpaired learning methods to address the limitations of data scarcity in specific application domains.