Generating High Dynamic Range Radiance Maps from a Single Image

被引:0
|
作者
Li, Xiaofen [1 ]
Huo, Yongqing [1 ]
机构
[1] Univ Elect Sci & Technol China, Sch Commun & Informat Engn, Chengdu 611731, Sichuan, Peoples R China
来源
PROCEEDINGS OF 2017 IEEE INTERNATIONAL CONFERENCE ON SIGNAL PROCESSING AND COMMUNICATION (ICSPC'17) | 2017年
基金
中国国家自然科学基金;
关键词
D O I
暂无
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
We present a method of generating high dynamic range (HDR) radiance maps from a single low dynamic range (LDR) image and its camera response function (CRF). The method first models and estimates the inverse CRF; then multiplies the inverse CRF by a weighting function to make it smooth near the maximum and minimum pixel values; finally conducts the smooth inverse CRF on the input LDR image to generate HDR image. In the method, the inverse CRF is estimated using one single LDR image and an empirical model of CRF, based on measured RGB distributions at color edges. Unlike most existing methods, the proposed method expands image from both high and low luminance region. Thus, the algorithm can avoid the artifacts and detail loss in dark area which results from extending image only from bright region. Extensive experimental results show that the approach induces less contrast distortion and produces high visual quality HDR image.
引用
收藏
页码:316 / 322
页数:7
相关论文
共 50 条
  • [21] Accuracy of luminance maps obtained from high dynamic range images
    Moeck, Martin
    LEUKOS, 2007, 4 (02) : 99 - 112
  • [22] Accuracy of luminance maps obtained from high dynamic range images
    Moeck, Martin
    LEUKOS - Journal of Illuminating Engineering Society of North America, 2007, 4 (02): : 99 - 112
  • [23] Reconstruction of a high dynamic range and high resolution image from a multisampled image sequence
    Haraldsson, Harald B.
    Tanaka, Masayuki
    Okutomi, Masatoshi
    14TH INTERNATIONAL CONFERENCE ON IMAGE ANALYSIS AND PROCESSING, PROCEEDINGS, 2007, : 303 - +
  • [24] Algorithm of generating music melody based on single-exposure high dynamic range digital image using convolutional neural network
    Cui, Jiayue
    Wang, Hongjun
    JOURNAL OF ELECTRONIC IMAGING, 2022, 31 (05)
  • [25] HDR-NeRF: High Dynamic Range Neural Radiance Fields
    Huang, Xin
    Zhang, Qi
    Feng, Ying
    Li, Hongdong
    Wang, Xuan
    Wang, Qing
    2022 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2022), 2022, : 18377 - 18387
  • [26] High Dynamic Range Imaging and Low Dynamic Range Expansion for Generating HDR Content
    Banterle, Francesco
    Debattista, Kurt
    Artusi, Alessandro
    Pattanaik, Sumanta
    Myszkowski, Karol
    Ledda, Patrick
    Chalmers, Alan
    COMPUTER GRAPHICS FORUM, 2009, 28 (08) : 2343 - 2367
  • [27] High Dynamic Range Image Acquisition from Multiple Low Dynamic Range Images Based on Estimation of Scene Dynamic Range
    Park, Kee-Hyon
    Park, Dae-Geun
    Ha, Yeong-Ho
    JOURNAL OF IMAGING SCIENCE AND TECHNOLOGY, 2009, 53 (02)
  • [28] High Dynamic Range Imaging on a Mobile Device Using Single Input Image
    Celebi, Aysun Tasyapi
    Duvar, Ramazan
    Urhan, Oguzhan
    2015 23RD SIGNAL PROCESSING AND COMMUNICATIONS APPLICATIONS CONFERENCE (SIU), 2015, : 1582 - 1585
  • [29] Tutorial: Luminance Maps for Daylighting Studies from High Dynamic Range Photography
    Pierson, C.
    Cauwerts, C.
    Bodart, M.
    Wienold, J.
    LEUKOS, 2021, 17 (02) : 140 - 169
  • [30] Generation of high-dynamic range image from digital photo
    Wang, Ying
    Potemin, Igor S.
    Zhdanov, Dmitry D.
    Wang, Xu-Yang
    Cheng, Han
    OPTOELECTRONIC IMAGING AND MULTIMEDIA TECHNOLOGY IV, 2016, 10020