Exposure Correction

Exposure correction in image and video processing aims to enhance visual data degraded by improper lighting conditions, encompassing both under- and overexposure, as well as mixed exposure scenarios. Current research heavily utilizes deep learning, employing convolutional neural networks (CNNs), transformers, and hybrid architectures to achieve real-time processing and high-quality results, often incorporating attention mechanisms and multi-scale processing for improved accuracy. These advancements are crucial for improving the performance of various computer vision applications, such as visual odometry, object detection, and 3D reconstruction, and enhancing the quality of user-generated content. Furthermore, research is exploring efficient model designs with minimal parameters and computational cost for deployment on resource-constrained devices.