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 flowwarped 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.

Overview

This is the co-author's reference implementation of the multi-image reflection removal using TensorFlow described in: "Learning to See Through Obstructions", based on Yu Lu Liu an these authors:

Yu-Lun Liu, Wei-Sheng Lai, Ming-Hsuan Yang, Yung-Yu Chuang, Jia-Bin Huang (National Taiwan University & Google & Virginia Tech & University of California at Merced & MediaTek Inc.)

This forked version focus on optimisations for low energy hardware devices like REBOTNIX GUSTAV (NANO), (TX2), (AGX XAVIER) which are based on NVIDIA JETSON Plattform.

in CVPR 2020. If you find this code useful for your research, please consider citing the following paper.

Further information for running this model on low energy devices onpremise or in the cloud, please contact [Gary Hilgemann] at(https://rebotnix.com).

Requirements setup

• TensorFlow:

  • tested using TensorFlow 1.10.0

• Pre-trained PWC-Net

  • Please overwrite tfoptflow/model_pwcnet.py and tfoptflow/model_base.py using the ones in this repository.

• To download the optimized pre-trained models for embedded devices like REBOTNIX GUSTAV and REBOTNIX Visiontools.

  UPLOAD PRETRAINED IN PROGRESS (Available Soon)

  • TENSORFLOW 1.10.0 is required and please make sure that you use the protobuf installation based on NVIDIA Jetpack 4.3 (other version will not work) ckpt

Data Preparation

Please prepare 5 frames and follow the naming rule XXXXX_I[0-4].png as shown in imgs folder, and change the folder path in run_reflection.py or test_fence.py.

[CVPR 2020]

Learning to See Through Obstructions

Usage

• Run your own sequence (reflection removal):

CUDA_VISIBLEDEVICES=0 python3 run_reflection.py

• Run your own sequence (fence removal):

CUDA_VISIBLEDEVICES=0 python3 test_fence.py

Citation

[1] Yu-Lun Liu, Wei-Sheng Lai, Ming-Hsuan Yang, Yung-Yu Chuang, and Jia-Bin Huang. Learning to See Through Obstructions. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2020