Abstract—A novel algorithm to remove rain or snow streaks

a video sequence using temporal correlation and low-rank

matrix completion is proposed in this paper. Based on the

observation that rain streaks are too small and move too fast

to affect the optical flow estimation between consecutive fr a mes,

we obtain an initial rain map by subtracting temporally warped

fr a mes a current fr a me. Then, we decompose the initial

rain map into basis vectors based on the sparse representation,

and classify those basis vectors into rain streak ones and outliers

with a support vector machine. We then refine the rain map

by excluding the outliers. Finally, we remove the detected rain

streaks by employing a low-rank matrix completion technique.

Furthermore, we extend the proposed algorithm to stereo video

deraining. Experimental results demonstrate that the proposed

algorithm detects and removes rain or snow streaks efficiently,

outperforming conventional algorithms.-Abstract—A novel algorithm to remove rain or snow streaks

a video sequence using temporal correlation and low-rank

matrix completion is proposed in this paper. Based on the

observation that rain streaks are too small and move too fast

to affect the optical flow estimation between consecutive fr a mes,

we obtain an initial rain map by subtracting temporally warped

fr a mes　a current fr a me. Then, we decompose the initial

rain map into basis vectors based on the sparse representation,

and classify those basis vectors into rain streak ones and outliers

with a support vector machine. We then refine the rain map

by excluding the outliers. Finally, we remove the detected rain

streaks by employing a low-rank matrix completion technique.

Furthermore, we extend the proposed algorithm to stereo video

deraining. Experimental results demonstrate that the proposed

algorithm detects and removes rain or snow streaks efficiently,

outperforming conventional algorithms.