RGB-NIR imaging with exposure bracketing for joint denoising and deblurring of low-light color images
Hiroki Yamashita, Daisuke Sugimura, Takayuki Hamamoto · 2017
Color images taken in low light scenes are deteriorated with noise and motion blur. The simultaneous reduction of noise and motion blur from the low-light color images is difficult because the imposed noise hinders accurate motion blur kernel estimation. To overcome this problem, we build a novel imaging system using a single sensor that captures red, green, blue (RGB) and near-infrared (NIR) images. Our imaging system captures low-light scenes with exposure bracketing, which is a technique to acquire multiple images with different exposure times. It thus allows us to obtain the short- and long-exposure RGB/NIR images. Both the short- and long-exposure NIR images taken using an NIR flash unit can be captured with less noise; thus they enable estimation of motion blur kernel accurately. Based on this fact, we perform joint denoising and deblurring of the low-light color image with the estimated motion blur kernel. Our experiments using real raw data captured by our imaging system demonstrate the effectiveness of our method.