Motion Blur Kernel

Motion blur kernel research focuses on understanding and mitigating the blurring effects caused by camera or object movement during image capture. Current efforts concentrate on developing sophisticated deep learning models, including encoder-decoder architectures and kernel prediction networks, to estimate and compensate for spatially-variant blur kernels—meaning the blur varies across the image. This research is significant because accurate blur kernel estimation is crucial for improving image deblurring techniques, leading to sharper and more detailed images in various applications, such as photography, video processing, and medical imaging.