Video De raining and Desnowing Using Temporal Correlation and Low-Rank Matrix Completion - 2015

PROJECT TITLE :

Video De raining and Desnowing Using Temporal Correlation and Low-Rank Matrix Completion - 2015

ABSTRACT:

A novel algorithm to remove rain or snow streaks from 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 quick to have an effect on the optical flow estimation between consecutive frames, we have a tendency to acquire an initial rain map by subtracting temporally warped frames from a current frame. Then, we decompose the initial rain map into basis vectors based mostly on the sparse representation, and classify those basis vectors into rain streak ones and outliers with a support vector machine. We have a tendency to then refine the rain map by excluding the outliers. Finally, we tend to take away the detected rain streaks by employing an occasional-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.

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