Demosaicing for RGBZ sensor
Lilong Shi, Ilia Ovsiannikov, Dong-Ki Min, Yohwan Noh, Wanghyun Kim, Sunhwa Jung, J. H. LEE, Deokha Shin, Hyekyung Jung, Gregory Waligorski, Yibing Michelle Wang, Wendy Wang, Yoondong Park, Chilhee Chung · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2013
In this paper, we proposed a new technique for demosaicing a unique RGBZ color-depth imaging sensor, which captures color and depth images simultaneously, with a specially designed color-filter-array (CFA) where two out of six RGB color rows are replaced by “Z” pixels that capture depth information but no color information. Therefore, in an RGBZ image, the red, green and blue colors are more sparsely sampled than in a standard Bayer image. Due to the missing rows in the data image, commonly used demosaicing algorithms for the standard Bayer CFA cannot be applied directly. To this end, our method first fills-in the missing rows to reconstruct a full Bayer CFA, followed by a color-selective adaptive demosaicing algorithm that interpolates missing color components. In the first step, unlike common bilinear interpolation approaches that tend to blur edges, our edge-based directional interpolation approach, derived from de-interlacing techniques, emphasizes reconstructing more straight and sharp edges with fewer artifacts and thereby preserves the vertical resolution in the reconstructed the image. In the second step, to avoid using the newly estimated pixels for demosaicing, the bilateral-filter-based approach interpolates the missing color samples based on weighted average of adaptively selected known pixels from the local neighborhoods. Tests show that the proposed method reconstructs full color images while preserving edges details, avoiding artifacts, and removing noise with high efficiency.