TY - GEN
T1 - Wide-Angle Rectification via Content-Aware Conformal Mapping
AU - Zhang, Qi
AU - Li, Hongdong
AU - Wang, Qing
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Despite the proliferation of ultra wide-angle lenses on smartphone cameras, such lenses often come with severe image distortion (e.g. curved linear structure, unnaturally skewed faces). Most existing rectification methods adopt a global warping transformation to undistort the input wideangle image, yet their performances are not entirely satisfactory, leaving many unwanted residue distortions uncorrected or at the sacrifice of the intended wide FoV (field- of-view). This paper proposes a new method to tackle these challenges. Specifically, we derive a locally-adaptive polardomain conformal mapping to rectify a wide-angle image. Parameters of the mapping are found automatically by analyzing image contents via deep neural networks. Experiments on a large number of photos have confirmed the superior performance of the proposed method compared with all available previous methods.
AB - Despite the proliferation of ultra wide-angle lenses on smartphone cameras, such lenses often come with severe image distortion (e.g. curved linear structure, unnaturally skewed faces). Most existing rectification methods adopt a global warping transformation to undistort the input wideangle image, yet their performances are not entirely satisfactory, leaving many unwanted residue distortions uncorrected or at the sacrifice of the intended wide FoV (field- of-view). This paper proposes a new method to tackle these challenges. Specifically, we derive a locally-adaptive polardomain conformal mapping to rectify a wide-angle image. Parameters of the mapping are found automatically by analyzing image contents via deep neural networks. Experiments on a large number of photos have confirmed the superior performance of the proposed method compared with all available previous methods.
KW - Optimization methods (other than deep learning)
UR - http://www.scopus.com/inward/record.url?scp=85173969051&partnerID=8YFLogxK
U2 - 10.1109/CVPR52729.2023.01665
DO - 10.1109/CVPR52729.2023.01665
M3 - 会议稿件
AN - SCOPUS:85173969051
T3 - Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
SP - 17357
EP - 17365
BT - Proceedings - 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2023
PB - IEEE Computer Society
T2 - 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2023
Y2 - 18 June 2023 through 22 June 2023
ER -