BlurBurst: Removing Blur Due to Camera Shake using Multiple Images

Image deblurring has matured over the last decade; today, there are a wide range of deblurring algorithms that operate successfully in the wild. Yet, there are many applications — including telephoto and low-light photography — where camera shake produces a blur kernel that is large enough to cripple state-of-the-art deblurring algorithms. This failure can be attributed to the decreasing SNR at the higher-frequencies of the latent image with increasing blur kernel size. As a consequence, resolving the finest details in the image is often impossible without undesirable artifacts due to noise amplification. In this paper, we demonstrate that these challenges can be overcome by obtaining multiple blurred images. We make the following observations. First, the burst mode in most digital cameras supports the ability to take a sequence of shots in rapid succession. Second, blur due to camera shake is largely one-dimensional; hence, just obtaining a few blurry images opportunistically produces blur orientations that are not aligned with each other; this produces dramatic improvements in deblurring. Third, an alternating sequence of convex programs can be used to recover both the latent image and blur kernels effectively. We refer to this multi-image deblurring algorithm as BlurBurst. We demonstrate applications of BlurBurst in telephoto and low-light photography and highlight broader uses in hand-held high dynamic-range (HDR) imaging.