Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/103305
Title: Semantic image content filtering via edge-preserving scale-aware filter
Authors: Ye, Wei
Ma, Kai-Kuang
Keywords: Semantic Image Content Filtering
Edge-preserving
Engineering::Electrical and electronic engineering
Issue Date: 2017
Source: Ye, W., & Ma, K.-K. (2017). Semantic image content filtering via edge-preserving scale-aware filter. 2017 IEEE International Conference on Image Processing (ICIP). doi:10.1109/ICIP.2017.8296721
Abstract: In this paper, we highlight a new filtering concept and methodology, called the semantic image content filtering (SICF), which aims to remove insignificant small details from the image while preserving its main structure. Such image content separation is not possible to achieve by using any conventional linear filter as it is essentially designed to perform frequency separation. To realize an effective SICF, a novel image filtering algorithm, called the edge-preserving scale-aware filter (ESF), is proposed in this paper. Our proposed ESF yields a significant improvement over a recently-developed scale-aware filter, called the rolling guidance filter (RGF). The key success of our ESF lies in the developed adaptive relative total variation filter (ARTVF), which replaces the RGF's Gaussian filter for generating a much improved initial guidance image. Extensive simulation results obtained from various test images have clearly demonstrated that the proposed ESF outperforms other state-of-the-art methods on conducting SICF task. That is, the semantically-important large-scale image structure has been better preserved, while the insignificant small details have been removed more effectively.
URI: https://hdl.handle.net/10356/103305
http://hdl.handle.net/10220/49993
DOI: http://dx.doi.org/10.1109/ICIP.2017.8296721
Rights: © 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/ICIP.2017.8296721
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Conference Papers

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