Motion-Compensated Spatial-Temporal Filtering for Noisy CFA Sequence

摘要

Spatial-temporal filters have been widely used in video denoising module. The filters are commonly designed for monochromatic image. However, most digital video cameras use a color filter array (CFA) to get color sequence. We propose a recursive spatial-temporal filter using motion estimation (ME) and motion compensated prediction (MCP) for CFA sequence. In the proposed ME method, we obtain candidate motion vectors from CFA sequence through hypothetical luminance maps. With the estimated motion vectors, the accurate MCP is obtained from CFA sequence by weighted averaging, which is determined by spatial-temporal LMMSE. Then, the temporal filter combines estimated MCP and current pixel. This process is controlled by the motion detection value. After temporal filtering, the spatial filter is applied to the filtered current frame as a post-processing. Experimental results show that the proposed method achieves good denoising performance without motion blurring and acquires high visual quality.