Decomposed Multilateral Filtering for Accelerating Filtering with Multiple Guidance Images
This paper proposes an efficient algorithm for edge-preserving filtering with multiple guidance images, so-called multilateral filtering. Multimodal signal processing for sensor fusion is increasingly important in image sensing. Edge-preserving filtering is available for various sensor fusion applic...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-01-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/24/2/633 |
| _version_ | 1797339328452493312 |
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| author | Haruki Nogami Yamato Kanetaka Yuki Naganawa Yoshihiro Maeda Norishige Fukushima |
| author_facet | Haruki Nogami Yamato Kanetaka Yuki Naganawa Yoshihiro Maeda Norishige Fukushima |
| author_sort | Haruki Nogami |
| collection | DOAJ |
| description | This paper proposes an efficient algorithm for edge-preserving filtering with multiple guidance images, so-called multilateral filtering. Multimodal signal processing for sensor fusion is increasingly important in image sensing. Edge-preserving filtering is available for various sensor fusion applications, such as estimating scene properties and refining inverse-rendered images. The main application is joint edge-preserving filtering, which can preferably reflect the edge information of a guidance image from an additional sensor. The drawback of edge-preserving filtering lies in its long computational time; thus, many acceleration methods have been proposed. However, most accelerated filtering cannot handle multiple guidance information well, although the multiple guidance information provides us with various benefits. Therefore, we extend the efficient edge-preserving filters so that they can use additional multiple guidance images. Our algorithm, named decomposes multilateral filtering (DMF), can extend the efficient filtering methods to the multilateral filtering method, which decomposes the filter into a set of constant-time filtering. Experimental results show that our algorithm performs efficiently and is sufficient for various applications. |
| first_indexed | 2024-03-08T09:46:24Z |
| format | Article |
| id | doaj.art-3806a92e5c9343e1924e593c874e08b9 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-08T09:46:24Z |
| publishDate | 2024-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-3806a92e5c9343e1924e593c874e08b92024-01-29T14:17:29ZengMDPI AGSensors1424-82202024-01-0124263310.3390/s24020633Decomposed Multilateral Filtering for Accelerating Filtering with Multiple Guidance ImagesHaruki Nogami0Yamato Kanetaka1Yuki Naganawa2Yoshihiro Maeda3Norishige Fukushima4Department of Computer Science, Faculty of Engineering, Nagoya Institute of Technology, Gokiso-cho, Showa-ku, Nagoya 466-8555, JapanDepartment of Computer Science, Faculty of Engineering, Nagoya Institute of Technology, Gokiso-cho, Showa-ku, Nagoya 466-8555, JapanDepartment of Computer Science, Faculty of Engineering, Nagoya Institute of Technology, Gokiso-cho, Showa-ku, Nagoya 466-8555, JapanDepartment of Electrical Engineering, Faculty of Engineering, Tokyo University of Science, Tokyo 125-8585, JapanDepartment of Computer Science, Faculty of Engineering, Nagoya Institute of Technology, Gokiso-cho, Showa-ku, Nagoya 466-8555, JapanThis paper proposes an efficient algorithm for edge-preserving filtering with multiple guidance images, so-called multilateral filtering. Multimodal signal processing for sensor fusion is increasingly important in image sensing. Edge-preserving filtering is available for various sensor fusion applications, such as estimating scene properties and refining inverse-rendered images. The main application is joint edge-preserving filtering, which can preferably reflect the edge information of a guidance image from an additional sensor. The drawback of edge-preserving filtering lies in its long computational time; thus, many acceleration methods have been proposed. However, most accelerated filtering cannot handle multiple guidance information well, although the multiple guidance information provides us with various benefits. Therefore, we extend the efficient edge-preserving filters so that they can use additional multiple guidance images. Our algorithm, named decomposes multilateral filtering (DMF), can extend the efficient filtering methods to the multilateral filtering method, which decomposes the filter into a set of constant-time filtering. Experimental results show that our algorithm performs efficiently and is sufficient for various applications.https://www.mdpi.com/1424-8220/24/2/633constant-time filteringedge-preserving filteringmultilateral filtering |
| spellingShingle | Haruki Nogami Yamato Kanetaka Yuki Naganawa Yoshihiro Maeda Norishige Fukushima Decomposed Multilateral Filtering for Accelerating Filtering with Multiple Guidance Images Sensors constant-time filtering edge-preserving filtering multilateral filtering |
| title | Decomposed Multilateral Filtering for Accelerating Filtering with Multiple Guidance Images |
| title_full | Decomposed Multilateral Filtering for Accelerating Filtering with Multiple Guidance Images |
| title_fullStr | Decomposed Multilateral Filtering for Accelerating Filtering with Multiple Guidance Images |
| title_full_unstemmed | Decomposed Multilateral Filtering for Accelerating Filtering with Multiple Guidance Images |
| title_short | Decomposed Multilateral Filtering for Accelerating Filtering with Multiple Guidance Images |
| title_sort | decomposed multilateral filtering for accelerating filtering with multiple guidance images |
| topic | constant-time filtering edge-preserving filtering multilateral filtering |
| url | https://www.mdpi.com/1424-8220/24/2/633 |
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