Dark Corner Artifact

Dark corner artifacts, regions of low image intensity, pose a significant challenge in various image analysis tasks, hindering accurate object detection and classification. Current research focuses on developing robust algorithms, often employing deep learning architectures like UNets and transformers, to either mitigate these artifacts through techniques such as inpainting or to design models that learn to effectively ignore or utilize them for improved performance. This work is crucial for advancing applications ranging from medical image analysis (e.g., skin cancer detection, corneal endothelium assessment) to autonomous navigation in low-light conditions and high-energy physics event detection, ultimately improving the reliability and accuracy of these systems.