Noise-robust image deblurring by blending regular- and short-exposure images

We propose a new image deblurring method using differently exposed image pair. Regular-exposure image has more blur but less noise, while short-exposure image has more noise but less blur. Conventional approaches blend the two images using only good features of them based on the difference between the degradations. Although these approaches are effective under normal conditions, it is difficult to distinguish blur from noise under low light conditions. So we made two improvements to deal with large noise. One is using the gradient information of the regular-exposure image to refine the motion blur detection. The other is that noise on the edges is effectively suppressed considering the edge shapes and the noise levels on each pixel in the blended image. Finally, we implemented our method on the digital still camera and we successfully obtained the higher-quality images with less blur and noise through the simulations as well as the real camera examinations.