Image Corruption

Image corruption, encompassing various degradations like noise, blur, and missing data, significantly impacts the performance of computer vision systems. Current research focuses on enhancing model robustness through techniques such as multimodal data fusion, novel convolutional architectures (e.g., incorporating inhibitory mechanisms), and adaptive test-time reconstruction methods. These advancements are crucial for improving the reliability and accuracy of applications ranging from autonomous driving and medical image analysis to robotics and e-commerce, where image quality is often compromised.