Journal
IEEE TRANSACTIONS ON IMAGE PROCESSING
Volume 23, Issue 12, Pages 5638-5653Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TIP.2014.2366600
Keywords
Edge-preserving smoothing (EPS); weighted least squares (WLS); fast global smoother (FGS); iterative re-weighted least squares (IRLS); aggregated data constraint; imprecise input
Funding
- Human Sixth Sense Programme at the Advanced Digital Sciences Center from Singapore's Agency for Science, Technology and Research (A*STAR)
- Direct For Computer & Info Scie & Enginr
- Division of Computing and Communication Foundations [1218682] Funding Source: National Science Foundation
- Div Of Information & Intelligent Systems
- Direct For Computer & Info Scie & Enginr [1116012] Funding Source: National Science Foundation
Ask authors/readers for more resources
This paper presents an efficient technique for performing a spatially inhomogeneous edge-preserving image smoothing, called fast global smoother. Focusing on sparse Laplacian matrices consisting of a data term and a prior term (typically defined using four or eight neighbors for 2D image), our approach efficiently solves such global objective functions. In particular, we approximate the solution of the memoryand computation-intensive large linear system, defined over a d-dimensional spatial domain, by solving a sequence of 1D subsystems. Our separable implementation enables applying a linear-time tridiagonal matrix algorithm to solve d three-point Laplacian matrices iteratively. Our approach combines the best of two paradigms, i.e., efficient edge-preserving filters and optimization-based smoothing. Our method has a comparable runtime to the fast edge-preserving filters, but its global optimization formulation overcomes many limitations of the local filtering approaches. Our method also achieves high-quality results as the state-of-the-art optimization-based techniques, but runs similar to 10-30 times faster. Besides, considering the flexibility in defining an objective function, we further propose generalized fast algorithms that perform L-gamma norm smoothing (0 < gamma < 2) and support an aggregated (robust) data term for handling imprecise data constraints. We demonstrate the effectiveness and efficiency of our techniques in a range of image processing and computer graphics applications.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available