Recent advances in image deblurring

Motion blur is a common artifact that produces disappointing blurry images with inevitable information loss. Due to the nature of imaging sensors that accumulates incoming lights, a motion blurred image will be obtained if the camera sensor moves during exposure. Image (motion) de-blurring is a computational process to remove motion blurs from a blurred image to obtain a sharp latent image. Recently image de-blurring has become a popular topic in computer graphics and vision research, and excellent methods have been developed to improve the quality of de-blurred images and accelerate the computation speed. Image de-blurring has also a variety of applications in image enhancement software and camera industry, and a practical image de-blurring method with quality and speed would be a critical factor to improve the performance of image enhancement and camera systems.

This course will first introduce the concepts, theoretical model, problem definition, and basic approach of image de-blurring. Blind deconvolution and non-blind deconvolution are two main topics of image de-blurring, which are classified by the existence of given kernel (or PSF; point spread function) information that describes the camera motion. For both blind deconvolution and non-blind deconvolution, challenges, classical methods, and recent research trends and successful methods will be presented. A PhotoShop demo will be given to show the performance of a recently developed fast motion de-blurring method.

This course will also cover several advanced issues of image de-blurring, such as hardware based approaches, spatially-varying camera shakes, object motions, and video de-blurring. It will conclude with remaining challenges, such as outliers and noise, computation time, and quality assessment. There will be Q&A at the end of each presentation with a short discussion at the end of the course.