Rank-One Prior: Toward Real-Time Scene Recovery
This addresses scene recovery for applications such as video surveillance and autonomous vehicles, but appears incremental as it builds on existing imaging methods.
The paper tackles the problem of scene recovery under degraded conditions like sandstorms, underwater, and haze by proposing a real-time light correction method, achieving improved visual quality with a transmission estimation complexity of O(N).
Scene recovery is a fundamental imaging task for several practical applications, e.g., video surveillance and autonomous vehicles, etc. To improve visual quality under different weather/imaging conditions, we propose a real-time light correction method to recover the degraded scenes in the cases of sandstorms, underwater, and haze. The heart of our work is that we propose an intensity projection strategy to estimate the transmission. This strategy is motivated by a straightforward rank-one transmission prior. The complexity of transmission estimation is $O(N)$ where $N$ is the size of the single image. Then we can recover the scene in real-time. Comprehensive experiments on different types of weather/imaging conditions illustrate that our method outperforms competitively several state-of-the-art imaging methods in terms of efficiency and robustness.