2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

Imaging Without Borders

2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

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Paper Detail

Paper IDMLR-APPL-IVASR-3.7
Paper Title ROBUST IMAGE OUTPAINTING WITH LEARNABLE IMAGE MARGINS
Authors Cheng-Yo Tan, Chiao-An Yang, Shang-Fu Chen, National Taiwan University, Taiwan; Meng-Lin Wu, Qualcomm, United States; Yu-Chiang Frank Wang, National Taiwan University, Taiwan
SessionMLR-APPL-IVASR-3: Machine learning for image and video analysis, synthesis, and retrieval 3
LocationArea E
Session Time:Tuesday, 21 September, 08:00 - 09:30
Presentation Time:Tuesday, 21 September, 08:00 - 09:30
Presentation Poster
Topic Applications of Machine Learning: Machine learning for image & video analysis, synthesis, and retrieval
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract Given a partial image input, image outpainting is to produce the desirable output by recovering or extending the surrounding image regions. While existing image outpainting methods achieve impressive results based on the recent advances of deep learning, they either lack the ability to extend image regions in arbitrary directions or require the filling image margins to be given in advance. To address this challenging task, we propose a unique deep learning framework for robust image outpainting, which consists of a margin prediction network and a teacher-student-based network for producing outpainted images. Our proposed model does not require image filling margins to be known beforehand, while both image appearance and perceptual feature consistencies can be jointly enforced. Our experiments quantitatively and qualitatively verify the effectiveness of our method, which is shown to perform favorably against baseline and state-of-the-art image outpainting works.