Abstract
Using dark channel prior—a kind of statistics of the haze-free outdoor images—to remove haze from a single image input is simple and effective. However, due to the use of soft matting algorithm, the method suffers from massive consumption of both memory and time, which largely limits its scalability for large images. In this paper, we present a hierarchical approach to accelerate dark channel based image dehazing. The core of our approach is a novel, efficient scheme for solving the soft matting problem involved in image dehazing, using adaptively subdivided quadtrees built in image space. Acceleration is achieved by transforming the problem of solving a N-variable linear system required in soft matting, to a problem of solving a much smaller m-variable linear system, where N is the number of pixels and m is the number of the corners in the quadtree. Our approach significantly reduces both space and time cost while still maintains visual fidelity, and largely extends the practicability of dark channel based image dehazing to handle large images.
Similar content being viewed by others
References
Narasimhan S G, Nayar S K. Chromatic framework for vision in bad weather. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. IEEE Comput Soc, 2000, 1: 598–605
Narasimhan S G, Nayar S K. Contrast restoration of weather degraded images. IEEE Trans Pattern Anal Mach Intell, 2003, 25: 713–724
Nayar S K, Narasimhan S G. Vision in bad weather. In: Proceedings of the 7th IEEE International Conference on Computer Vision. IEEE Comput Soc, 1999, 2: 820–827
Shwartz S, Namer E, Schechner Y Y. Blind haze separation. In: Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Washington, 2006. 1984–1991
Schechner Y Y, Narasimhan S G, Nayar S K. Instant dehazing of images using polarization. In: Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. IEEE Comput Soc, 2001, 1: 325–332
Kopf J, Neubert B, Chen B, et al. Deep photo: Model-based photograph enhancement and viewing. In: Proceedings of SIGGRAPH Asia 2008. ACM Trans Graph, 2008, 27: 116
Huang H, **. Vis Comput, 2010, 26: 731–738
Fattal R. Single image dehazing. ACM Trans Graph, 2008, 27: 1–9
Tan R T. Visibility in bad weather from a single image. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Los Alamitos, 2008. 1–8
Zhang J W, Li L, Yang G Q, et al. Local albedo-insensitive single image dehazing. Vis Comput, 2010, 26: 761–768
He K M, Sun J, Tang X O. Single image haze removal using dark channel prior. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2009. 1956–1963
Levin A, Lischinski D, Weiss Y. A closed form solution to natural image matting. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2006. 1–9
Levin A, Lischinski D, Weiss Y. A closed-form solution to natural image matting. IEEE Trans Pattern Anal Mach Intell, 2008, 30: 228–242
Agarwala A. Efficient gradient-domain compositing using quadtrees. ACM Trans Graph, 2007, 26: 94
Xu K, Li Y, Ju T, et al. Efficient affinity-based edit propagation using k-d tree. ACM Trans Graph, 2009, 28: 118
Samet H. Applications of spatial data structures: computer graphics, image processing, and GIS / Hanan Samet. Boston: Addison-Wesley, 1990
Davis T A, Hager W W. Dynamic supernodes in sparse cholesky update/downdate and triangular solves. ACM Trans Math Softw, 2009, 35: 1–23
Chen Y Q, Davis T A, Hager W W, et al. Algorithm 887: Cholmod, supernodal sparse cholesky factorization and update/downdate. ACM Trans Math Softw, 2008, 35: 1–14
Davis T. Pcg vs backslash in matlab. 2009. http://www.cise.ufl.edu/research/sparse
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Ding, M., Tong, R. Efficient dark channel based image dehazing using quadtrees. Sci. China Inf. Sci. 56, 1–9 (2013). https://doi.org/10.1007/s11432-012-4566-y
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11432-012-4566-y