A hybrid camera for motion deblurring and depth map super-resolution

被引:0
|
作者
Li, Feng [1 ]
Yu, Jingyi [1 ]
Chai, Jinxiang [2 ]
机构
[1] Univ Delaware, Dept Comp & Informat Sci, Newark, DE 19716 USA
[2] Texas A&M Univ, Dept Comp Sci, College Stn, TX 77843 USA
基金
美国国家科学基金会;
关键词
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
We present a hybrid camera that combines the advantages of a high resolution camera and a high speed camera. Our hybrid camera consists of a pair of low-resolution high-speed (LRHS) cameras and a single high-resolution low-speed (HRLS) camera. The LRHS cameras are able to capture fast-motion with little motion blur They also form a stereo pair and provide a low-resolution depth map. The HRLS camera provides a high spatial resolution but also introduces severe motion blur when capturing fast moving objects. We develop efficient algorithms to simultaneously motion-deblur the HRLS image and reconstruct a high resolution depth map. Our method estimates the motion flow in the LRHS pair and then warps the flow field to the HRLS camera to estimate the point spread function (PSF). We then motion-deblur the HRLS image and use the resulting image to enhance the low-resolution depth map using joint bilateral filters. We demonstrate the hybrid camera in depth map super-resolution and motion deblurring with spatially varying kernels. Experiments show that our framework is robust and highly effective.
引用
收藏
页码:1803 / +
页数:2
相关论文
共 50 条
  • [31] Depth Map Super-Resolution Reconstruction Based on Convolutional Neural Networks
    Li S.
    Lei G.
    Fan R.
    [J]. Lei, Guoqing (lgq20051118@163.com), 2017, Chinese Optical Society (37):
  • [32] Jitter camera: a super-resolution video camera
    Ben-Ezra, M
    Zomet, A
    Nayar, SK
    [J]. VISUAL COMMUNICATIONS AND IMAGE PROCESSING 2006, PTS 1 AND 2, 2006, 6077
  • [33] Joint depth map super-resolution method via deep hybrid-cross guidance filter
    Wang, Ke
    Zhao, Lijun
    Zhang, Jinjing
    Zhang, Jialong
    Wang, Anhong
    Bai, Huihui
    [J]. PATTERN RECOGNITION, 2023, 136
  • [34] A REVISIT TO MRF-BASED DEPTH MAP SUPER-RESOLUTION AND ENHANCEMENT
    Lu, Jiangbo
    Min, Dongbo
    Pahwa, Ramanpreet Singh
    Do, Minh N.
    [J]. 2011 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING, 2011, : 985 - 988
  • [35] Single Depth Map Super-resolution with Local Self-similarity
    Wang, Xiaochuan
    Wang, Kai
    Liang, Xiaohui
    [J]. PROCEEDINGS OF 2018 THE 2ND INTERNATIONAL CONFERENCE ON VIDEO AND IMAGE PROCESSING (ICVIP 2018), 2018, : 198 - 202
  • [36] Single depth map super-resolution via a deep feedback network
    Wu, Guoliang
    Wang, Yanjie
    Li, Shi
    [J]. INTERNATIONAL JOURNAL OF WAVELETS MULTIRESOLUTION AND INFORMATION PROCESSING, 2021, 19 (02)
  • [37] Edge-Preserving Depth Map Super-Resolution with Intensity Guidance
    Xiaochuan Wang
    Xiaohui Liang
    [J]. Journal of Beijing Institute of Technology, 2019, 28 (01) : 51 - 56
  • [38] Fast Depth Map Super-Resolution using Deep Neural Network
    Korinevskaya, Alisa
    Makarov, Ilya
    [J]. ADJUNCT PROCEEDINGS OF THE 2018 IEEE INTERNATIONAL SYMPOSIUM ON MIXED AND AUGMENTED REALITY (ISMAR), 2018, : 117 - 122
  • [39] Hierarchical Features Driven Residual Learning for Depth Map Super-Resolution
    Guo, Chunle
    Li, Chongyi
    Guo, Jichang
    Cong, Runmin
    Fu, Huazhu
    Han, Ping
    [J]. IEEE TRANSACTIONS ON IMAGE PROCESSING, 2019, 28 (05) : 2545 - 2557
  • [40] Discrete Cosine Transform Network for Guided Depth Map Super-Resolution
    Zhao, Zixiang
    Zhang, Jiangshe
    Xu, Shuang
    Lin, Zudi
    Pfister, Hanspeter
    [J]. 2022 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2022), 2022, : 5687 - 5697