HumanAware Motion Deblurring. This paper proposes a humanaware deblurring model that disentangles the motion blur between foreground (FG) humans and background (BG). The proposed model is based on a triplebranch encoderdecoder architecture. The first two branches are learned for sharpening FG humans and BG details, respectively; while the third one produces global, harmonious results by comprehensively fusing multiscale deblurring information from the two domains. The proposed model is further endowed with a supervised, humanaware attention mechanism in an endtoend fashion. It learns a soft mask that encodes FG human information and explicitly drives the FGBG decoderbranches to focus on their specific domains. To further benefit the research towards Humanaware Image Deblurring, we introduce a largescale dataset, named HIDE, which consists of 8,422 blurry and sharp image pairs with 65,784 densely annotated FG human bounding boxes. HIDE is specifically built to span a broad range of scenes, human object sizes, motion patterns, and background complexities. Extensive experiments on public benchmarks and our dataset demonstrate that our model performs favorably against the stateoftheart motion deblurring methods, especially in capturing semantic details.

A Coarsetofine Classification for Motion Blur Kernel Size Estimation with Cascaded Neural Networks Blur kernel size estimation significantly affects the quality of image deblurring. However, existing methods have a large limitation in estimation accuracy as well as in the adaptability to large kernel sizes. In this paper, we propose a novel blur kernel size estimation method using CNNs. We first convert the 2D kernel size estimation to a 1D classification problem. A cascaded network model using homogeneous networks is then designed to cut the entire estimation space into 38 small intervals to have a more accurate kernel size estimation in a coarsetofine manner. Experiments show that the proposed method has reached a satisfying performance of kernel size estimation with a largely improved adaptability to various blur kernels.