A Novel Tensor-based Video Rain Streaks Removal Approach via Utilizing
Discriminatively Intrinsic Priors
Abstract
Rain streaks removal is an important issue of the outdoor
vision system and has been recently investigated extensively. In this paper, we propose a novel tensor based video
rain streaks removal approach by fully considering the discriminatively intrinsic characteristics of rain streaks and
clean videos, which needs neither rain detection nor timeconsuming dictionary learning stage. In specific, on the one
hand, rain streaks are sparse and smooth along the raindrops’ direction, and on the other hand, the clean videos
possess smoothness along the rain-perpendicular direction
and global and local correlation along time direction. We
use the l1 norm to enhance the sparsity of the underlying
rain streaks, two unidirectional Total Variation (TV) regularizers to guarantee the different discriminative smoothness, and a tensor nuclear norm and a time directional difference operator to characterize the exclusive correlation of
the clean video along time. Alternation direction method of
multipliers (ADMM) is employed to solve the proposed concise tensor based convex model. Experiments implemented
on synthetic and real data substantiate the effectiveness and
efficiency of the proposed method. Under comprehensive
quantitative performance measures, our approach outperforms other state-of-the-art methods.