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Edge-Aware Filter Based on Adaptive Patch Variance Weighted Average

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posted on 20.09.2021, 05:13 by Fernando GalettoFernando Galetto, Guang DengGuang Deng, M Al-Nasrawi, Waseem WaheedWaseem Waheed
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.

Funding

The work of Fernando J. Galetto was supported in part by a La Trobe University Graduate Research Scholarship (LTGRS) and in part by Full Fee Research Scholarship (LTUFFRS).

History

Publication Date

24/08/2021

Journal

IEEE Access

Volume

9

Pagination

(p. 118291-118306)

Publisher

IEEE

ISSN

2169-3536

Rights Statement

This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/