Liu Jun, Yan Ming, Zeng Tieyong
IEEE Trans Pattern Anal Mach Intell. 2021 Mar;43(3):1041-1055. doi: 10.1109/TPAMI.2019.2941472. Epub 2021 Feb 4.
Blind image deblurring is a conundrum because there are infinitely many pairs of latent image and blur kernel. To get a stable and reasonable deblurred image, proper prior knowledge of the latent image and the blur kernel is urgently required. Different from the recent works on the statistical observations of the difference between the blurred image and the clean one, our method is built on the surface-aware strategy arising from the intrinsic geometrical consideration. This approach facilitates the blur kernel estimation due to the preserved sharp edges in the intermediate latent image. Extensive experiments demonstrate that our method outperforms the state-of-the-art methods on deblurring the text and natural images. Moreover, our method can achieve attractive results in some challenging cases, such as low-illumination images with large saturated regions and impulse noise. A direct extension of our method to the non-uniform deblurring problem also validates the effectiveness of the surface-aware prior.
盲图像去模糊是一个难题，因为存在无数对潜在图像和模糊核。为了获得稳定且合理的去模糊图像，迫切需要关于潜在图像和模糊核的适当先验知识。与最近关于模糊图像和清晰图像之间差异的统计观测的工作不同，我们的方法基于从内在几何考虑产生的表面感知策略。由于中间潜在图像中保留了清晰边缘，这种方法便于模糊核估计。大量实验表明，我们的方法在对文本和自然图像去模糊方面优于当前的先进方法。此外，我们的方法在一些具有挑战性的情况下可以取得有吸引力的结果，例如具有大饱和区域的低光照图像和脉冲噪声。将我们的方法直接扩展到非均匀去模糊问题也验证了表面感知先验的有效性。
