Asymmetric, non-unimodal kernel regression for image processing

Damith J. Mudugamuwa; Wenjing Jia; Xiangjian He

doi:10.1109/DICTA.2010.34

Asymmetric, non-unimodal kernel regression for image processing

Damith J. Mudugamuwa
, Wenjing Jia
, Xiangjian He

Research output: Chapter in Book/Conference proceeding › Conference contribution › peer-review

Abstract

Kernel regression has been previously proposed as a robust estimator for a wide range of image processing tasks, including image denoising, interpolation and super-resolution. In this article we propose a kernel formulation that relaxes the usual symmetric and unimodal properties to effectively exploit the smoothness characteristics of natural images. The proposed method extends the kernel support along similar image characteristics to further increase the robustness of the estimates. Application of the proposed method to image denoising yields significant improvement over the previously reported regression methods and produces results comparable to the state-of-the-art denoising techniques.

Original language	English
Title of host publication	Proceedings - 2010 Digital Image Computing
Subtitle of host publication	Techniques and Applications, DICTA 2010
Publisher	IEEE Computer Society
Pages	141-145
Number of pages	5
ISBN (Print)	9780769542713
DOIs	https://doi.org/10.1109/DICTA.2010.34
Publication status	Published - 2010
Externally published	Yes

Publication series

Name	Proceedings - 2010 Digital Image Computing: Techniques and Applications, DICTA 2010

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 11 Sustainable Cities and Communities

ASJC Scopus subject areas

Computational Theory and Mathematics
Computer Science Applications

Access to Document

10.1109/DICTA.2010.34

Cite this

Mudugamuwa, D. J., Jia, W., & He, X. (2010). Asymmetric, non-unimodal kernel regression for image processing. In Proceedings - 2010 Digital Image Computing: Techniques and Applications, DICTA 2010 (pp. 141-145). Article 5692555 (Proceedings - 2010 Digital Image Computing: Techniques and Applications, DICTA 2010). IEEE Computer Society. https://doi.org/10.1109/DICTA.2010.34

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title = "Asymmetric, non-unimodal kernel regression for image processing",

abstract = "Kernel regression has been previously proposed as a robust estimator for a wide range of image processing tasks, including image denoising, interpolation and super-resolution. In this article we propose a kernel formulation that relaxes the usual symmetric and unimodal properties to effectively exploit the smoothness characteristics of natural images. The proposed method extends the kernel support along similar image characteristics to further increase the robustness of the estimates. Application of the proposed method to image denoising yields significant improvement over the previously reported regression methods and produces results comparable to the state-of-the-art denoising techniques.",

author = "Mudugamuwa, \{Damith J.\} and Wenjing Jia and Xiangjian He",

year = "2010",

doi = "10.1109/DICTA.2010.34",

language = "English",

isbn = "9780769542713",

series = "Proceedings - 2010 Digital Image Computing: Techniques and Applications, DICTA 2010",

publisher = "IEEE Computer Society",

pages = "141--145",

booktitle = "Proceedings - 2010 Digital Image Computing",

address = "United States",

}

Asymmetric, non-unimodal kernel regression for image processing. / Mudugamuwa, Damith J.; Jia, Wenjing; He, Xiangjian.
Proceedings - 2010 Digital Image Computing: Techniques and Applications, DICTA 2010. IEEE Computer Society, 2010. p. 141-145 5692555 (Proceedings - 2010 Digital Image Computing: Techniques and Applications, DICTA 2010).

Research output: Chapter in Book/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Asymmetric, non-unimodal kernel regression for image processing

AU - Mudugamuwa, Damith J.

AU - Jia, Wenjing

AU - He, Xiangjian

PY - 2010

Y1 - 2010

N2 - Kernel regression has been previously proposed as a robust estimator for a wide range of image processing tasks, including image denoising, interpolation and super-resolution. In this article we propose a kernel formulation that relaxes the usual symmetric and unimodal properties to effectively exploit the smoothness characteristics of natural images. The proposed method extends the kernel support along similar image characteristics to further increase the robustness of the estimates. Application of the proposed method to image denoising yields significant improvement over the previously reported regression methods and produces results comparable to the state-of-the-art denoising techniques.

AB - Kernel regression has been previously proposed as a robust estimator for a wide range of image processing tasks, including image denoising, interpolation and super-resolution. In this article we propose a kernel formulation that relaxes the usual symmetric and unimodal properties to effectively exploit the smoothness characteristics of natural images. The proposed method extends the kernel support along similar image characteristics to further increase the robustness of the estimates. Application of the proposed method to image denoising yields significant improvement over the previously reported regression methods and produces results comparable to the state-of-the-art denoising techniques.

UR - https://www.scopus.com/pages/publications/79951642846

U2 - 10.1109/DICTA.2010.34

DO - 10.1109/DICTA.2010.34

M3 - Conference contribution

AN - SCOPUS:79951642846

SN - 9780769542713

T3 - Proceedings - 2010 Digital Image Computing: Techniques and Applications, DICTA 2010

SP - 141

EP - 145

BT - Proceedings - 2010 Digital Image Computing

PB - IEEE Computer Society

ER -

Asymmetric, non-unimodal kernel regression for image processing

Abstract

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