Real-time Image Smoothing via Iterative Least Squares

Author:

Liu Wei1ORCID,Zhang Pingping2,Huang Xiaolin3,Yang Jie3,Shen Chunhua1,Reid Ian1

Affiliation:

1. School of Computer Science, The University of Adelaide, Australia

2. School of Information and Communication Engineering, Dalian University of Technology, China

3. Institute of Image Processing and Pattern Recognition 8 Institute of Medical Robotics, Shanghai Jiao Tong University

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.

Funder

Ministry of Science and Technology

Australian Research Council

ARC Laureate Fellowship

Committee of Science and Technology

National Natural Science Foundation of China

National Key Research Development

Publisher

Association for Computing Machinery (ACM)

Subject

Computer Graphics and Computer-Aided Design

Reference73 articles.

1. Martín Abadi Ashish Agarwal Paul Barham Eugene Brevdo Zhifeng Chen Craig Citro Greg S. Corrado Andy Davis Jeffrey Dean Matthieu Devin Sanjay Ghemawat Ian Goodfellow Andrew Harp Geoffrey Irving Michael Isard Yangqing Jia Rafal Jozefowicz Lukasz Kaiser Manjunath Kudlur Josh Levenberg Dan Mané Rajat Monga Sherry Moore Derek Murray Chris Olah Mike Schuster Jonathon Shlens Benoit Steiner Ilya Sutskever Kunal Talwar Paul Tucker Vincent Vanhoucke Vijay Vasudevan Fernanda Viégas Oriol Vinyals PeteWarden MartinWattenberg Martin Wicke Yuan Yu and Xiaoqiang Zheng. 2015. TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Retrieved from http://tensorflow.org/ Software available from tensorflow.org Martín Abadi Ashish Agarwal Paul Barham Eugene Brevdo Zhifeng Chen Craig Citro Greg S. Corrado Andy Davis Jeffrey Dean Matthieu Devin Sanjay Ghemawat Ian Goodfellow Andrew Harp Geoffrey Irving Michael Isard Yangqing Jia Rafal Jozefowicz Lukasz Kaiser Manjunath Kudlur Josh Levenberg Dan Mané Rajat Monga Sherry Moore Derek Murray Chris Olah Mike Schuster Jonathon Shlens Benoit Steiner Ilya Sutskever Kunal Talwar Paul Tucker Vincent Vanhoucke Vijay Vasudevan Fernanda Viégas Oriol Vinyals PeteWarden MartinWattenberg Martin Wicke Yuan Yu and Xiaoqiang Zheng. 2015. TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Retrieved from http://tensorflow.org/ Software available from tensorflow.org

2. Fast Image Recovery Using Variable Splitting and Constrained Optimization

3. Fast Local Laplacian Filters

4. Hicham Badri Hussein Yahia and Driss Aboutajdine. 2013. Fast multi-scale detail decomposition via accelerated iterative shrinkage. In SIGGRAPH Asia 2013 Technical Briefs. ACM 33. Hicham Badri Hussein Yahia and Driss Aboutajdine. 2013. Fast multi-scale detail decomposition via accelerated iterative shrinkage. In SIGGRAPH Asia 2013 Technical Briefs. ACM 33.

Cited by 75 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. A semantic edge-aware parameter efficient image filtering technique;Computers & Graphics;2024-11

2. Hyperbolic tangent penalty function for edge-preserving image filtering;Digital Signal Processing;2024-10

3. Contrast-preserving image smoothing via the truncated first-order rational function;Signal Processing;2024-09

4. Iterative Self-Guided Image Filtering;IEEE Transactions on Circuits and Systems for Video Technology;2024-08

5. Fast Global Image Smoothing via Quasi Weighted Least Squares;International Journal of Computer Vision;2024-07-13

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3