We present a learning-based approach for removing unwanted obstructions, such as window reflections, fence occlusions or raindrops, from a short sequence of images captured by a moving camera. Our method leverages the motion differences between the background and the obstructing elements to recover both layers. Specifically, we alternate between estimating dense optical flow fields of the two layers and reconstructing each layer from the flow-warped images via a deep convolutional neural network. The learning-based layer reconstruction allows us to accommodate potential errors in the flow estimation and brittle assumptions such as brightness consistency. We show that training on synthetically generated data transfers well to real images. Our results on numerous challenging scenarios of reflection and fence removal demonstrate the effectiveness of the proposed method.
|Number of pages||10|
|Journal||Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition|
|Publication status||Published - 2020|
|Event||2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2020 - Virtual, Online, United States|
Duration: 2020 Jun 14 → 2020 Jun 19
Bibliographical noteFunding Information:
Acknowledgments. This work is supported in part by NSF CAREER (#1149783), NSF CRII (#1755785), MOST 109-2634-F-002-032, MediaTek Inc. and gifts from Adobe, Toyota, Panasonic, Samsung, NEC, Verisk, and Nvidia.
All Science Journal Classification (ASJC) codes
- Computer Vision and Pattern Recognition