Evaluating parameterization methods for convolutional neural network (CNN)-based image operators

Seung Wook Kim; Sung Jin Cho; Kwang Hyun Uhm; Seo Won Ji; Sang Won Lee; Sung Jea Ko

doi:10.1109/CVPRW.2019.00237

Evaluating parameterization methods for convolutional neural network (CNN)-based image operators

Seung Wook Kim, Sung Jin Cho, Kwang Hyun Uhm, Seo Won Ji, Sang Won Lee, Sung Jea Ko

School of Electrical Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Citation (Scopus)

Abstract

Recently, deep neural networks have been widely used to approximate or improve image operators. In general, an image operator has some hyper-parameters that change its operating configurations, e.g., the strength of smoothing, up-scale factors in super-resolution, or a type of image operator. To address varying parameter settings, an image operator taking such parameters as its input, namely a parameterized image operator, is an essential cue in image processing. Since many types of parameterization techniques exist, a comparative analysis is required in the context of image processing. In this paper, we therefore analytically explore the operation principles of these parameterization techniques and study their differences. In addition, performance comparisons between image operators parameterized by using these methods are assessed experimentally on common image processing tasks including image smoothing, denoising, deblocking, and super-resolution.

Original language	English
Title of host publication	Proceedings - 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2019
Publisher	IEEE Computer Society
Pages	1862-1870
Number of pages	9
ISBN (Electronic)	9781728125060
DOIs	https://doi.org/10.1109/CVPRW.2019.00237
Publication status	Published - 2019 Jun
Event	32nd IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2019 - Long Beach, United States Duration: 2019 Jun 16 → 2019 Jun 20

Publication series

Name	IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
Volume	2019-June
ISSN (Print)	2160-7508
ISSN (Electronic)	2160-7516

Conference

Conference	32nd IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2019
Country/Territory	United States
City	Long Beach
Period	19/6/16 → 19/6/20

Bibliographical note

Funding Information:
This work was supported by Institute of Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (No.2014-3-00077, Development of global multi-target tracking and event prediction techniques based on real-time large-scale analysis)

Publisher Copyright:
© 2019 IEEE.

ASJC Scopus subject areas

Computer Vision and Pattern Recognition
Electrical and Electronic Engineering

Access to Document

10.1109/CVPRW.2019.00237

Cite this

Kim, S. W., Cho, S. J., Uhm, K. H., Ji, S. W., Lee, S. W., & Ko, S. J. (2019). Evaluating parameterization methods for convolutional neural network (CNN)-based image operators. In Proceedings - 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2019 (pp. 1862-1870). Article 9025633 (IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops; Vol. 2019-June). IEEE Computer Society. https://doi.org/10.1109/CVPRW.2019.00237

Evaluating parameterization methods for convolutional neural network (CNN)-based image operators. / Kim, Seung Wook; Cho, Sung Jin; Uhm, Kwang Hyun et al.
Proceedings - 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2019. IEEE Computer Society, 2019. p. 1862-1870 9025633 (IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops; Vol. 2019-June).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Kim, SW, Cho, SJ, Uhm, KH, Ji, SW, Lee, SW & Ko, SJ 2019, Evaluating parameterization methods for convolutional neural network (CNN)-based image operators. in Proceedings - 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2019., 9025633, IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, vol. 2019-June, IEEE Computer Society, pp. 1862-1870, 32nd IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2019, Long Beach, United States, 19/6/16. https://doi.org/10.1109/CVPRW.2019.00237

Kim SW, Cho SJ, Uhm KH, Ji SW, Lee SW, Ko SJ. Evaluating parameterization methods for convolutional neural network (CNN)-based image operators. In Proceedings - 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2019. IEEE Computer Society. 2019. p. 1862-1870. 9025633. (IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops). doi: 10.1109/CVPRW.2019.00237

Kim, Seung Wook ; Cho, Sung Jin ; Uhm, Kwang Hyun et al. / Evaluating parameterization methods for convolutional neural network (CNN)-based image operators. Proceedings - 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2019. IEEE Computer Society, 2019. pp. 1862-1870 (IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops).

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title = "Evaluating parameterization methods for convolutional neural network (CNN)-based image operators",

abstract = "Recently, deep neural networks have been widely used to approximate or improve image operators. In general, an image operator has some hyper-parameters that change its operating configurations, e.g., the strength of smoothing, up-scale factors in super-resolution, or a type of image operator. To address varying parameter settings, an image operator taking such parameters as its input, namely a parameterized image operator, is an essential cue in image processing. Since many types of parameterization techniques exist, a comparative analysis is required in the context of image processing. In this paper, we therefore analytically explore the operation principles of these parameterization techniques and study their differences. In addition, performance comparisons between image operators parameterized by using these methods are assessed experimentally on common image processing tasks including image smoothing, denoising, deblocking, and super-resolution.",

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AB - Recently, deep neural networks have been widely used to approximate or improve image operators. In general, an image operator has some hyper-parameters that change its operating configurations, e.g., the strength of smoothing, up-scale factors in super-resolution, or a type of image operator. To address varying parameter settings, an image operator taking such parameters as its input, namely a parameterized image operator, is an essential cue in image processing. Since many types of parameterization techniques exist, a comparative analysis is required in the context of image processing. In this paper, we therefore analytically explore the operation principles of these parameterization techniques and study their differences. In addition, performance comparisons between image operators parameterized by using these methods are assessed experimentally on common image processing tasks including image smoothing, denoising, deblocking, and super-resolution.

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Evaluating parameterization methods for convolutional neural network (CNN)-based image operators

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