TY - GEN
T1 - ABPN
T2 - 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2022
AU - Lei, Biwen
AU - Guo, Xiefan
AU - Yang, Hongyu
AU - Cui, Miaomiao
AU - Xie, Xuansong
AU - Huang, Di
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Photo retouching finds many applications in various fields. However, most existing methods are designed for global retouching and seldom pay attention to the local region, while the latter is actually much more tedious and time-consuming in photography pipelines. In this paper, we propose a novel adaptive blend pyramid network, which aims to achieve fast local retouching on ultra high-resolution photos. The network is mainly composed of two components: a context-aware local retouching layer (LRL) and an adaptive blend pyramid layer (BPL). The LRL is designed to implement local retouching on low-resolution images, giving full consideration of the global context and local texture information, and the BPL is then developed to progressively expand the low-resolution results to the higher ones, with the help of the proposed adaptive blend module and refining module. Our method outperforms the existing methods by a large margin on two local photo retouching tasks and exhibits excellent performance in terms of running speed, achieving real-time inference on 4K images with a single NVIDIA Tesla P100 GPU. Moreover, we introduce the first high-definition cloth retouching dataset CRHD-3K to promote the research on local photo retouching. The dataset is available at https://github.com/youngLbw/crhd-3K.
AB - Photo retouching finds many applications in various fields. However, most existing methods are designed for global retouching and seldom pay attention to the local region, while the latter is actually much more tedious and time-consuming in photography pipelines. In this paper, we propose a novel adaptive blend pyramid network, which aims to achieve fast local retouching on ultra high-resolution photos. The network is mainly composed of two components: a context-aware local retouching layer (LRL) and an adaptive blend pyramid layer (BPL). The LRL is designed to implement local retouching on low-resolution images, giving full consideration of the global context and local texture information, and the BPL is then developed to progressively expand the low-resolution results to the higher ones, with the help of the proposed adaptive blend module and refining module. Our method outperforms the existing methods by a large margin on two local photo retouching tasks and exhibits excellent performance in terms of running speed, achieving real-time inference on 4K images with a single NVIDIA Tesla P100 GPU. Moreover, we introduce the first high-definition cloth retouching dataset CRHD-3K to promote the research on local photo retouching. The dataset is available at https://github.com/youngLbw/crhd-3K.
KW - Datasets and evaluation
KW - Image and video synthesis and generation
KW - Low-level vision
UR - https://www.scopus.com/pages/publications/85141599347
U2 - 10.1109/CVPR52688.2022.00215
DO - 10.1109/CVPR52688.2022.00215
M3 - 会议稿件
AN - SCOPUS:85141599347
T3 - Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
SP - 2098
EP - 2107
BT - Proceedings - 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2022
PB - IEEE Computer Society
Y2 - 19 June 2022 through 24 June 2022
ER -