Learning to Estimate and Remove Non-uniform Image Blur
By Florent Couzinié-Devy, Jian Sun, Alahari Karteek, Jean Ponce. 2013
106 citations recorded by the source corpus.
Subfields: Computer Vision and Pattern Recognition, Media Technology
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computer Vision and Pattern Recognition
- Ming-Hsuan Yang
- Luc Van Gool
- Chunhua Shen
- Dacheng Tao
- Chen Change Loy
- Xiaogang Wang
- Anton van den Hengel
- Jitendra Malik
In Media Technology
- Kyoung Mu Lee
- Tae Hyun Kim
- Zongben Xu
- Chunhua Shen
- Jian Sun
- Wenfei Cao
- Ian David Reid
- Ming-Hsuan Yang
Citations in the corpus
- From Motion Blur to Motion Flow: A Deep Learning Solution for Removing Heterogeneous Motion Blur (2016, background): “Recently, learning-based discriminative methods [4, 7] have been proposed to learn blur image patterns and avoid the heavy computational cost of blur estimation.”
- Learning a convolutional neural network for non-uniform motion blur removal (2015, methodology): ““SLayerRegr” is the extension of approach in [3].”
- Segmentation-Free Dynamic Scene Deblurring (2014, background): “extended the work of Levin in [4] by casting the problem as a multi-label segmentation problem and estimating locally varying blur.”