In this paper, we consider numerous low-level vision problems (e.g., edge-preserving filtering and denoising) as recursive image filtering via a hybrid neural network. The network contains several spatially variant recurrent neural networks (RNN) as equivalents of a group of distinct recursive filters for each pixel, and a deep convolutional neural network (CNN) that learns the weights of RNNs. The deep CNN can learn regulations of recurrent propagation for various tasks and effectively guides recurrent propagation over an entire image. The proposed model does not need a large number of convolutional channels nor big kernels to learn features for low-level vision filters. It is significantly smaller and faster in comparison with a deep CNN based image filter. Experimental results show that many low-level vision tasks can be effectively learned and carried out in real-time by the proposed algorithm.
|Title of host publication||Computer Vision - 14th European Conference, ECCV 2016, Proceedings|
|Editors||Bastian Leibe, Jiri Matas, Nicu Sebe, Max Welling|
|Number of pages||17|
|Publication status||Published - 2016|
|Event||14th European Conference on Computer Vision, ECCV 2016 - Amsterdam, Netherlands|
Duration: 2016 Oct 8 → 2016 Oct 16
|Name||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Conference||14th European Conference on Computer Vision, ECCV 2016|
|Period||16/10/8 → 16/10/16|
Bibliographical noteFunding Information:
This work is supported in part by the NSF CAREER grant #1149783, NSF IIS grant #1152576, gifts from Adobe/Nvidia Preliminary work is carried out at Multimedia Laboratory in Chinese University of Hong Kong.
All Science Journal Classification (ASJC) codes
- Theoretical Computer Science
- Computer Science(all)