RawSeg: Grid Spatial and Spectral Attended Semantic Segmentation Based on Raw Bayer Images

Semantic segmentation methods are typically designed for RGB color images, which are interpolated from raw Bayer images. While RGB images provide abundant color information and are easily understood by humans, they also add extra storage and computational burden for neural networks. On the other hand, raw Bayer images preserve primitive color information with a single channel, potentially increasing segmentation accuracy while significantly decreasing storage and computation time. In this paper, we propose RawSeg-Net to segment single-channel raw Bayer images directly. Different from RGB images that already contain neighboring context information during ISP color interpolation, each pixel in raw Bayer images does not contain any context clues. Based on Bayer pattern properties, RawSeg-Net assigns dynamic attention on Bayer images’ spectral frequency and spatial locations to mitigate classification confusion, and proposes a re-sampling strategy to capture both global and local contextual information.