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https://hdl.handle.net/2440/129434
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Type: | Journal article |
Title: | Real-time image smoothing via iterative least squares |
Author: | Liu, W. Zhang, P. Huang, X. Yang, J. Shen, C. Reid, I. |
Citation: | ACM Transactions on Graphics, 2020; 39(3):28-1-28-24 |
Publisher: | Association for Computing Machinery |
Issue Date: | 2020 |
ISSN: | 0730-0301 1557-7368 |
Statement of Responsibility: | Wei Liu, Pingping Zhang, Xiaolin Huang, Jie Yang, Chunhua Shen and Ian Reid |
Abstract: | Edge-preserving image smoothing is a fundamental procedure for many computer vision and graphic applications. There is a tradeoff between the smoothing quality and the processing speed: the high smoothing quality usually requires a high computational cost, which leads to the low processing speed. In this article, we propose a new global optimization based method, named iterative least squares (ILS), for efficient edge-preserving image smoothing. Our approach can produce high-quality results but at a much lower computational cost. Comprehensive experiments demonstrate that the proposed method can produce results with little visible artifacts. Moreover, the computation of ILS can be highly parallel, which can be easily accelerated through either multi-thread computing or the GPU hardware. With the acceleration of a GTX 1080 GPU, it is able to process images of 1080p resolution (1920 × 1080) at the rate of 20fps for color images and 47fps for gray images. In addition, the ILS is flexible and can be modified to handle more applications that require different smoothing properties. Experimental results of several applications show the effectiveness and efficiency of the proposed method. The code is available at https://github.com/wliusjtu/Real-time-Image-Smoothing-via-Iterative-Least-Squares. |
Keywords: | Edge-preserving smoothing; iterative least squares (ILS); global methods; image detail enhancement; high dynamic range (HDR) tone mapping; texture smoothing; clip-art compression artifacts removal |
Rights: | © 2020 Association for Computing Machinery. |
DOI: | 10.1145/3388887 |
Grant ID: | http://purl.org/au-research/grants/arc/CE140100016 http://purl.org/au-research/grants/arc/FL130100102 |
Published version: | http://dx.doi.org/10.1145/3388887 |
Appears in Collections: | Aurora harvest 8 Computer Science publications |
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