Spatial-Separated Curve Rendering Network for Efficient and High-Resolution Image Harmonization

• URL: http://arxiv.org/abs/2109.05750v2
• Date: Tue, 14 Sep 2021 08:02:51 GMT
• Title: Spatial-Separated Curve Rendering Network for Efficient and High-Resolution Image Harmonization
• Authors: Jingtang Liang, Xiaodong Cun and Chi-Man Pun
• Abstract summary: We propose a novel spatial-separated curve rendering network (S$2$CRNet) for efficient and high-resolution image harmonization. The proposed method reduces more than 90% parameters compared with previous methods. Our method can work smoothly on higher resolution images in real-time which is more than 10$times$ faster than the existing methods.
• Score: 59.19214040221055
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Image harmonization aims to modify the color of the composited region with respect to the specific background. Previous works model this task as a pixel-wise image-to-image translation using UNet family structures. However, the model size and computational cost limit the performability of their models on edge devices and higher-resolution images. To this end, we propose a novel spatial-separated curve rendering network (S$^2$CRNet) for efficient and high-resolution image harmonization for the first time. In S$^2$CRNet, we firstly extract the spatial-separated embeddings from the thumbnails of the masked foreground and background individually. Then, we design a curve rendering module (CRM), which learns and combines the spatial-specific knowledge using linear layers to generate the parameters of the pixel-wise curve mapping in the foreground region. Finally, we directly render the original high-resolution images using the learned color curve. Besides, we also make two extensions of the proposed framework via the Cascaded-CRM and Semantic-CRM for cascaded refinement and semantic guidance, respectively. Experiments show that the proposed method reduces more than 90% parameters compared with previous methods but still achieves the state-of-the-art performance on both synthesized iHarmony4 and real-world DIH test set. Moreover, our method can work smoothly on higher resolution images in real-time which is more than 10$\times$ faster than the existing methods. The code and pre-trained models will be made available and released at https://github.com/stefanLeong/S2CRNet.

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