TY - GEN
T1 - Under-Display Camera Image Restoration with Scattering Effect
AU - Song, Binbin
AU - Chen, Xiangyu
AU - Xu, Shuning
AU - Zhou, Jiantao
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - The under-display camera (UDC) provides consumers with a full-screen visual experience without any obstruction due to notches or punched holes. However, the semitransparent nature of the display inevitably introduces the severe degradation into UDC images. In this work, we address the UDC image restoration problem with the specific consideration of the scattering effect caused by the display. We explicitly model the scattering effect by treating the display as a piece of homogeneous scattering medium. With the physical model of the scattering effect, we improve the image formation pipeline for the image synthesis to construct a realistic UDC dataset with ground truths. To suppress the scattering effect for the eventual UDC image recovery, a two-branch restoration network is designed. More specifically, the scattering branch leverages global modeling capabilities of the channel-wise self-attention to estimate parameters of the scattering effect from degraded images. While the image branch exploits the local representation advantage of CNN to recover clear scenes, implicitly guided by the scattering branch. Extensive experiments are conducted on both real-world and synthesized data, demonstrating the superiority of the proposed method over the state-of-the-art UDC restoration techniques. The source code and dataset are available at https://github.com/NamecantbeNULL/SRUDC.
AB - The under-display camera (UDC) provides consumers with a full-screen visual experience without any obstruction due to notches or punched holes. However, the semitransparent nature of the display inevitably introduces the severe degradation into UDC images. In this work, we address the UDC image restoration problem with the specific consideration of the scattering effect caused by the display. We explicitly model the scattering effect by treating the display as a piece of homogeneous scattering medium. With the physical model of the scattering effect, we improve the image formation pipeline for the image synthesis to construct a realistic UDC dataset with ground truths. To suppress the scattering effect for the eventual UDC image recovery, a two-branch restoration network is designed. More specifically, the scattering branch leverages global modeling capabilities of the channel-wise self-attention to estimate parameters of the scattering effect from degraded images. While the image branch exploits the local representation advantage of CNN to recover clear scenes, implicitly guided by the scattering branch. Extensive experiments are conducted on both real-world and synthesized data, demonstrating the superiority of the proposed method over the state-of-the-art UDC restoration techniques. The source code and dataset are available at https://github.com/NamecantbeNULL/SRUDC.
UR - https://www.scopus.com/pages/publications/85185871326
U2 - 10.1109/ICCV51070.2023.01156
DO - 10.1109/ICCV51070.2023.01156
M3 - 会议稿件
AN - SCOPUS:85185871326
T3 - Proceedings of the IEEE International Conference on Computer Vision
SP - 12546
EP - 12555
BT - Proceedings - 2023 IEEE/CVF International Conference on Computer Vision, ICCV 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2023 IEEE/CVF International Conference on Computer Vision, ICCV 2023
Y2 - 2 October 2023 through 6 October 2023
ER -