Paper Info
Title
Auto coder-decoder (CODEC) model based sparse representation for image super resolution
Abstract
In our daily life, the high quality image is widely used in varieties of fields, but sometimes we cannot capture the image with idea resolution due to some influences. For solving the resolution limitation of imaging sensors, the image super resolution (SR) representation technology is widely researched. Considering the advantage of sparse representation, the dictionary learning based methods is widely studied. However, landmark atoms cannot provide the representations of images, since the general feature extractors is universally applicable in feature extraction. To overcome the drawbacks, an auto coder-decoder (CODEC) model is proposed to extract representative features from low resolution (LR) images. The experimental results indicate the proposed method can obtain better effect than other methods.
Year
DOI
Venue
2017
10.1109/CISP-BMEI.2017.8301950
2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)
Keywords
Field
DocType
Auto Cdoer-Decoder (CODEC) Model,Data-Dependent Feature Extractor (DDFE),Sparse Representation,Super Resolution (SR)
Iterative reconstruction,Computer vision,Dictionary learning,Pattern recognition,Computer science,Sparse approximation,Feature extraction,Artificial intelligence,Landmark,Image resolution,Superresolution,Codec
Conference
ISBN
Citations 
PageRank 
978-1-5386-1938-4
0
0.34
References 
Authors
0
5
Name
Order
Citations
PageRank
Qieshi Zhang11310.44
Liyan Gu200.34
jun cheng385169.84
Xiaojun Wu4229.54
Sei-ichiro Kamata518352.09