Comparison of motion de-blur algorithms and real world deployment

If a camera moves fast while taking a picture, motion blur is induced. There exist techniques to prevent this effect to occur, such as moving the lens system or the CCD chip electro-mechanically. Another approach is to remove the motion blur after the images have been taken, using signal processing algorithms as post-processing techniques. For more than 30 years, numerous researchers have developed theories and algorithms for this purpose, which work quite well when applied to artificially blurred images. If one attempts to use those techniques to real world scenarios, they mostly fail miserably. In order to study why the known algorithms have problems to de-blur naturally blurred images we have built an experimental setup, which produces real blurred images with defined parameters in a controlled environment. For this article we have studied the most important algorithms used for de-blurring, we have analyzed their properties when applied to artificially blurred images and to real images. We propose solutions to make the algorithms fit for purpose.