  Complex blur such as the mixup of space-variant and space-invariant blur,
which is hard to model mathematically, widely exists in real images. In this
paper, we propose a novel image deblurring method that does not need to
estimate blur kernels. We utilize a pair of images that can be easily acquired
in low-light situations: (1) a blurred image taken with low shutter speed and
low ISO noise; and (2) a noisy image captured with high shutter speed and high
ISO noise. Slicing the blurred image into patches, we extend the Gaussian
mixture model (GMM) to model the underlying intensity distribution of each
patch using the corresponding patches in the noisy image. We compute patch
correspondences by analyzing the optical flow between the two images. The
Expectation Maximization (EM) algorithm is utilized to estimate the parameters
of GMM. To preserve sharp features, we add an additional bilateral term to the
objective function in the M-step. We eventually add a detail layer to the
deblurred image for refinement. Extensive experiments on both synthetic and
real-world data demonstrate that our method outperforms state-of-the-art
techniques, in terms of robustness, visual quality, and quantitative metrics.
