Auto Coder-Decoder (CODEC) Model based Sparse Representation for Image Super Resolution
文献类型：会议论文
| 作者 | Qieshi Zhang; Liyan Gu; Jun Cheng; Xiaojun Wu |
| 出版日期 | 2017 |
| 会议地点 | 中国上海 |
| 英文摘要 | 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 coderdecoder (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. |
| 语种 | 英语 |
| 源URL | [http://ir.siat.ac.cn:8080/handle/172644/11828]  |
| 专题 | 深圳先进技术研究院_集成所 |
| 作者单位 | 2017 |
| 推荐引用方式 GB/T 7714 | Qieshi Zhang,Liyan Gu,Jun Cheng,et al. Auto Coder-Decoder (CODEC) Model based Sparse Representation for Image Super Resolution[C]. 见:. 中国上海. |
入库方式： OAI收割
来源：深圳先进技术研究院
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