An Experimental Comparative Performance Study of Demosaicing Algorithms on General-purpose GPUs

Abstract

The image registration by digital still cameras and video cameras requires color filters to be posed onto the photosensitive sensors (CCD or CMOS). The filters are arranged in patterns across the face of the image sensing array. The conventional color filter array (CFA) capture only one color component at each image pixel. The missing colors in the raw sensor data are interpolated by a process called CFA interpolation or demosaicing. Quality of the full-color reconstruction process is mostly relied on demosaicing method applied. Most of the current demosaicing methods are computationally expensive and often too slow for real-time scenarios. Many industrial applications require real-time and high quality demosaicing solutions, and quite often slow image reconstruction process is a real bottleneck. The purpose of this research is to present a comparative performance study of demosaicing algorithms on general-purpose GPUs. The experimental results of CUDA-based implementations of two state-of-the-art and widely applied in practice CFA algorithms are presented. The performance efficiency is assessed and analyzed by experimental studies on a set of real photographic test images on two general-purpose graphic cards. The obtained results demonstrated the benefit of exploiting the contemporary GPUs in speeding up the demosaicing process, especially for practical applications that need to meet real-time and high-speed video processing requirements combined with high quality of the full-color image reconstruction.