Acceleration of variance of color differences-based demosaicing using CUDA

Abstract

Image demosaicing algorithms are used to reconstruct a full color image from the incomplete color samples output (RAW data) of an image sensor overlaid with a Color Filter Array (CFA). Better demosaicing algorithms are superior in terms of acuity, dynamic range, signal to noise ratio, and artifact suppression, which make them suitable for high quality delivery such as theatrical broadcast. In this paper, we present our efforts in examining the feasibility of exploiting the Graphics Processing Unit (GPU) as an emerging accelerator to create an on-the-fly implementation of Variance of Color Differences (VCD) demosaicing, a state-of-the-art heuristic demosaicing algorithm developed to eliminate false-color artifacts in texture region of images. Our efforts in this paper are 1) implementing the algorithm as several kernels to separate the bottleneck portion of the algorithm from the rest and to minimize idle threads and 2) reducing I/O between shared and global memory when performing green channel interpolation by separating the input RAW data into four channels. We then compare the implementation featuring both acceleration methods with a single kernel implementation. Based on experimental results, our proposed acceleration methods achieved per-frame processing time of 343 ms on an nVidia GTX 480, which translates into 2.95 fps. Additionally, our proposed methods were also able to accelerate the kernel time and the effective memory bandwidth by a factor of 2.1× compared with its single kernel counterpart.