Abstract
Edge-aware smoothing is an essential tool for computer vision, graphics and photography. In this paper, we develop a new and efficient local weighted average filter for edge-aware smoothing. The proposed filter can use guidance information which permits an iterative filtering process. Since the weights of the proposed filter depend on the local variance, the implementation requires linear filters only, leading to $\mathcal {O}(N_{pix})$ computational complexity. We also present statistical analysis and simulations which provide new insights into its computational efficiency and its relationship with the bilateral filter. The performance of the proposed filter is comparable to those state-of-the-art filters in many applications including: edge-preserving smoothing, compression artifact removal, structure separation, edge extraction, non-photo realistic image rendering, salience detection, detail magnification and multi-focus image fusion.
Highlights
Filters which smooth image details are essential tools for many low-level vision applications [1]
An intriguing question is: can we develop a new local weighted average filter in the form of equation (1)? The new filter should retain the computational efficiency of the guided filter, and should avoid the computational complexity of the bilateral filter
We further show that the proposed filter retains the same O(Npix) computational complexity as that of the guided filter, and produces comparable or better results in a wide range of applications where edge-aware filters are required
Summary
Filters which smooth image details are essential tools for many low-level vision applications [1]. Such filters have been historically developed based on linear time-invariant (LTI) systems [2]. As a result of this obliviousness to edges, LTI filters produce artifacts such as halo and blurriness. To tackle this problem, edge-aware filters (EAF) [3] which are based on non-linear techniques have emerged as powerful tools for a wide range of applications in image processing and computer vision [4]– [10]. EAF can be classified into four categories: local, global, transform domain, and data-driven
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