Video deblurring algorithm using an adjacent unblurred frame

Blurred frames may sparsely exist in a video sequence acquired by digital camcorder or digital camera. In order to remove the visually annoying artifact due to those blurred frames, this paper presents a novel motion deblurring algorithm where a blurred frame can be reconstructed utilizing adjacent unblurred frames. Firstly, a motion-compensated predictor of the blurred frame is derived from its neighboring unblurred frame using motion estimation. Then, an accurate blur kernel, which is difficult to obtain from a single blurred frame, is computed using both the predictor and the blurred frame. Next, again using those both frames, a residual deconvolution is proposed to reduce ringing artifacts inherent to conventional deconvolution. Simulation results show that the proposed algorithm provides superior deblurring results over conventional deblurring algorithms while preserving details with reduced ringing artifacts.