Reflection removal under fast forward camera motion

The image quality of an in-vehicle black box camera is often degraded by the reflections of internal objects, dirt, and dust on the windshield. In this paper, we propose a novel algorithm that simultaneously removes the reflections and small dirt artifacts from in-vehicle black box videos under fast forward camera motion. The algorithm exploits the spatiotemporal coherence of the reflection and dirt, which remain stationary relative to the fast-moving background. Unlike previous algorithms, the algorithm first separates stationary reflection and then restores the background scene. To this end, we propose an average image prior, thereby imposing spatiotemporal coherence. The separation model is a two-layer model composed of stationary and background layers, where different gradient sparsity distributions are utilized in a region-based manner. Motion compensation in postprocessing is proposed to alleviate layer jitter due to vehicle vibrations. In evaluation experiments, the proposed algorithm successfully extracts the stationary layer from several real and synthetic black box videos.