Handling motion blur in multi-frame super-resolution
Refereed conference paper presented and published in conference proceedings

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AbstractUbiquitous motion blur easily fails multi-frame super-resolution (MFSR). Our method proposed in this paper tackles this issue by optimally searching least blurred pixels in MFSR. An EM framework is proposed to guide residual blur estimation and high-resolution image reconstruction. To suppress noise, we employ a family of sparse penalties as natural image priors, along with an effective solver. Theoretical analysis is performed on how and when our method works. The relationship between estimation errors of motion blur and the quality of input images is discussed. Our method produces sharp and higher-resolution results given input of challenging low-resolution noisy and blurred sequences.
All Author(s) ListMa Z., Liao R., Tao X., Xu L., Jia J., Wu E.
Name of ConferenceIEEE Conference on Computer Vision and Pattern Recognition, CVPR 2015
Start Date of Conference07/06/2015
End Date of Conference12/06/2015
Place of ConferenceBoston
Country/Region of ConferenceUnited States of America
Volume Number07-12-June-2015
Pages5224 - 5232
LanguagesEnglish-United Kingdom

Last updated on 2021-12-09 at 23:46