Missing Pixel

Missing pixel problems encompass various challenges related to reconstructing or handling incomplete image data, ranging from filling in occluded regions in images to mitigating noise and defects in sensor data. Current research focuses on developing sophisticated algorithms, often employing deep learning architectures like autoencoders and neural networks, to effectively predict missing pixel values based on surrounding information or learned image representations. These advancements have significant implications for diverse applications, including improving the quality of astronomical images, enhancing medical imaging, and enabling more efficient data annotation for tasks like semantic segmentation.