Blur Removal via Blurred-Noisy Image Pair
This addresses image deblurring for real-world applications where blur is hard to model, offering a practical solution for low-light photography, though it is incremental as it builds on existing patch-based and GMM methods.
The paper tackles the problem of removing complex blur from images without estimating blur kernels, by using a pair of blurred and noisy images and a Gaussian mixture model with optical flow and bilateral terms, achieving state-of-the-art performance in robustness, visual quality, and quantitative metrics.
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.