Hand-held Video Deblurring via Efficient Fourier Aggregation
This addresses blur removal in videos for photographers and users of hand-held devices, though it is incremental as it builds on existing Fourier-based methods with efficiency improvements.
The paper tackles video deblurring from hand-held camera shake by aggregating information from nearby frames in the Fourier domain, achieving state-of-the-art results with significantly faster processing than competitors.
Videos captured with hand-held cameras often suffer from a significant amount of blur, mainly caused by the inevitable natural tremor of the photographer's hand. In this work, we present an algorithm that removes blur due to camera shake by combining information in the Fourier domain from nearby frames in a video. The dynamic nature of typical videos with the presence of multiple moving objects and occlusions makes this problem of camera shake removal extremely challenging, in particular when low complexity is needed. Given an input video frame, we first create a consistent registered version of temporally adjacent frames. Then, the set of consistently registered frames is block-wise fused in the Fourier domain with weights depending on the Fourier spectrum magnitude. The method is motivated from the physiological fact that camera shake blur has a random nature and therefore, nearby video frames are generally blurred differently. Experiments with numerous videos recorded in the wild, along with extensive comparisons, show that the proposed algorithm achieves state-of-the-art results while at the same time being much faster than its competitors.