Edge-Aware Filter Based on Adaptive Patch Variance Weighted Average

• Main Authors: Fernando J. Galetto, Guang Deng, Mukhalad Al-Nasrawi, Waseem Waheed
• Format: Article
• Language: English
• Published: IEEE 2021-01-01
• Series: IEEE Access
• Subjects: Edge-aware smoothing; bilateral filter; guided filter; detail magnification; multi-focus image fusion
• Online Access: https://ieeexplore.ieee.org/document/9521149/

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 <inline-formula> <tex-math notation="LaTeX">$\mathcal {O}(N_{pix})$ </tex-math></inline-formula> 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.