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

    Research output: Chapter in Book/Report/Conference proceedingConference 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 languageEnglish
    Title of host publicationProceedings - 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2019
    PublisherIEEE Computer Society
    Pages1862-1870
    Number of pages9
    ISBN (Electronic)9781728125060
    DOIs
    Publication statusPublished - 2019 Jun
    Event32nd IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2019 - Long Beach, United States
    Duration: 2019 Jun 162019 Jun 20

    Publication series

    NameIEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
    Volume2019-June
    ISSN (Print)2160-7508
    ISSN (Electronic)2160-7516

    Conference

    Conference32nd IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2019
    Country/TerritoryUnited States
    CityLong Beach
    Period19/6/1619/6/20

    Bibliographical note

    Publisher Copyright:
    © 2019 IEEE.

    ASJC Scopus subject areas

    • Computer Vision and Pattern Recognition
    • Electrical and Electronic Engineering

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