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CVPR
2012
IEEE
13 years 11 hour ago
Unsupervised feature learning framework for no-reference image quality assessment
In this paper, we present an efficient general-purpose objective no-reference (NR) image quality assessment (IQA) framework based on unsupervised feature learning. The goal is to...
Peng Ye, Jayant Kumar, Le Kang, David S. Doermann
ICCV
2009
IEEE
1318views Computer Vision» more  ICCV 2009»
16 years 2 months ago
Non-Local Sparse Models for Image Restoration
We propose in this paper to unify two different ap- proaches to image restoration: On the one hand, learning a basis set (dictionary) adapted to sparse signal descriptions has p...
Julien Mairal, Francis Bach, Jean Ponce, Guillermo...
CVPR
2012
IEEE
13 years 11 hour ago
Bilevel sparse coding for coupled feature spaces
In this paper, we propose a bilevel sparse coding model for coupled feature spaces, where we aim to learn dictionaries for sparse modeling in both spaces while enforcing some desi...
Jianchao Yang, Zhaowen Wang, Zhe Lin, Xianbiao Shu...
ICPR
2010
IEEE
14 years 9 months ago
Incorporating Linguistic Model Adaptation into Whole-Book Recognition
Abstract—Whole-book recognition is a document image analysis strategy that operates on the complete set of a book’s page images using automatic adaptation to improve accuracy. ...
Pingping Xiu, Henry S. Baird
121
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ICASSP
2011
IEEE
14 years 1 months ago
Single-frame-based rain removal via image decomposition
Rain removal from a video is a challenging problem and has been recently investigated extensively. Nevertheless, the problem of rain removal from a single image has been rarely st...
Yu-Hsiang Fu, Li-Wei Kang, Chia-Wen Lin, Chiou-Tin...