Proceedings Article | 14 February 2013
Lilong Shi, Ilia Ovsiannikov, Dong-Ki Min, Yohwan Noh, Wanghyun Kim, Sunhwa Jung, Joonho Lee, Deokha Shin, Hyekyung Jung, Gregory Waligorski, Yibing Wang, Wendy Wang, Yoondong Park, Chilhee Chung
Proc. SPIE. 8657, Computational Imaging XI
KEYWORDS: Optical filters, Sensors, Image processing, Video, Digital filtering, Ions, Image sensors, Image filtering, Reconstruction algorithms, RGB color model
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.
