Hdr Image Saliency Estimation by Convex Optimization

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    1 Citation (Scopus)

    Abstract

    In this paper, we propose a convex optimization-based method for the visual saliency prediction of high dynamic range (HDR) images, which allows straightforward reuse of any existing saliency estimation methods for typical images with low dynamic range (LDR). First, the proposed method decomposes a given HDR image into multiple LDR images with different levels of intensity using a tone-mapping-based synthesis of imaginary multiexposure images. For each decomposed image, a standard saliency estimation method is then applied for typical LDR images. Finally, the saliency map of each decomposed image is integrated into a single map by solving convex optimization problems. The proposed method is applied to actual HDR images and its effectiveness is demonstrated.

    Original languageEnglish
    Title of host publication2020 IEEE International Conference on Image Processing, ICIP 2020 - Proceedings
    PublisherIEEE Computer Society
    Pages458-462
    Number of pages5
    ISBN (Electronic)9781728163956
    DOIs
    Publication statusPublished - Oct 2020
    Event2020 IEEE International Conference on Image Processing, ICIP 2020 - Virtual, Abu Dhabi, United Arab Emirates
    Duration: Sept 25 2020Sept 28 2020

    Publication series

    NameProceedings - International Conference on Image Processing, ICIP
    Volume2020-October
    ISSN (Print)1522-4880

    Conference

    Conference2020 IEEE International Conference on Image Processing, ICIP 2020
    Country/TerritoryUnited Arab Emirates
    CityVirtual, Abu Dhabi
    Period9/25/209/28/20

    All Science Journal Classification (ASJC) codes

    • Software
    • Computer Vision and Pattern Recognition
    • Signal Processing

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