Learn to model blurry motion via directional similarity and filtering

PATTERN RECOGNITION

卷:75

Article

Li, Wenbin^1,2  Chen, Da³  Lv, Zhihan²  Yan, Yan⁴  Cosker, Darren⁵ 

[1] Imperial Coll London, Dept Comp, London, England

[2] UCL, Dept Comp Sci, London, England

[3] Univ Bath, Dept Comp Sci, Bath, Avon, England

[4] Univ Trento, Dept Informat Engn & Comp Sci DISI, Trento, Italy

[5] Univ Bath, CAMERA, Bath, Avon, England

关键词: Optical flow;    Convolutional Neural Network (CNN);    Video/image deblurring;    Directional filtering;   

DOI  :  10.1016/j.patcog.2017.04.020

来源: Elsevier

【 摘 要 】

It is difficult to recover the motion field from a real-world footage given a mixture of camera shake and other photometric effects. In this paper we propose a hybrid framework by interleaving a Convolutional Neural Network (CNN) and a traditional optical flow energy. We first conduct a CNN architecture using a novel learnable directional filtering layer. Such layer encodes the angle and distance similarity matrix between blur and camera motion, which is able to enhance the blur features of the camera-shake footages. The proposed CNNs are then integrated into an iterative optical flow framework, which enable the capability of modeling and solving both the blind deconvolution and the optical flow estimation problems simultaneously. Our framework is trained end-to-end on a synthetic dataset and yields competitive precision and performance against the state-of-the-art approaches. (C) 2017 Elsevier Ltd. All rights reserved.

【 授权许可】

Free