Video Reconstruction

Video reconstruction focuses on recovering high-quality video frames from incomplete, compressed, or degraded data sources. Current research emphasizes developing efficient deep learning models, including autoencoders, recurrent networks, and transformers, to address challenges like limited bandwidth, computational constraints, and noisy sensor data. These advancements improve video quality in applications such as super-resolution, compression, and reconstruction from novel sensor modalities (e.g., event cameras), impacting fields ranging from video streaming to computer vision and robotics. Furthermore, research explores reconstructing videos from other data sources, such as brain activity or single blurred images, opening new avenues for understanding human perception and enhancing imaging capabilities.