Hu Zhe, Cho Sunghyun, Wang Jue, Yang Ming-Hsuan
IEEE Trans Pattern Anal Mach Intell. 2018 Oct;40(10):2329-2341. doi: 10.1109/TPAMI.2017.2768365. Epub 2017 Nov 2.
Images acquired in low-light conditions with handheld cameras are often blurry, so steady poses and long exposure time are required to alleviate this problem. Although significant advances have been made in image deblurring, state-of-the-art approaches often fail on low-light images, as a sufficient number of salient features cannot be extracted for blur kernel estimation. On the other hand, light streaks are common phenomena in low-light images that have not been extensively explored in existing approaches. In this work, we propose an algorithm that utilizes light streaks to facilitate deblurring low-light images. The light streaks, which commonly exist in the low-light blurry images, contain rich information regarding camera motion and blur kernels. A method is developed in this work to detect light streaks for kernel estimation. We introduce a non-linear blur model that explicitly takes light streaks and corresponding light sources into account, and pose them as constraints for estimating the blur kernel in an optimization framework. For practical applications, the proposed algorithm is extended to handle images undergoing non-uniform blur. Experimental results show that the proposed algorithm performs favorably against the state-of-the-art methods on deblurring real-world low-light images.
手持相机在低光照条件下拍摄的图像往往模糊不清，因此需要稳定的姿势和较长的曝光时间来缓解这一问题。尽管在图像去模糊方面已经取得了显著进展，但由于无法提取足够数量的显著特征用于模糊核估计，当前的先进方法在低光照图像上往往效果不佳。另一方面，光线条纹是低光照图像中的常见现象，现有方法对此尚未进行广泛研究。在这项工作中，我们提出了一种利用光线条纹来促进低光照图像去模糊的算法。光线条纹通常存在于低光照模糊图像中，包含有关相机运动和模糊核的丰富信息。本文开发了一种用于检测光线条纹以进行核估计的方法。我们引入了一种非线性模糊模型，该模型明确考虑了光线条纹和相应的光源，并将它们作为在优化框架中估计模糊核的约束条件。对于实际应用，我们将所提出的算法进行扩展，以处理非均匀模糊的图像。实验结果表明，在对真实世界的低光照图像进行去模糊处理时，所提出的算法优于当前的先进方法。
