Name
Abstract | ||
|---|---|---|
Recently, deep convolutional neural network (CNN) has achieved great success for image restoration (IR) and provided hierarchical features at the same time. However, most deep CNN based IR models do not make full use of the hierarchical features from the original low-quality images; thereby, resulting in relatively-low performance. In this work, we propose a novel and efficient residual dense netw... |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/TPAMI.2020.2968521 | IEEE Transactions on Pattern Analysis and Machine Intelligence |
Keywords | DocType | Volume |
Feature extraction,Image restoration,Training,Task analysis,Image coding,Image denoising | Journal | 43 |
Issue | ISSN | Citations |
7 | 0162-8828 | 22 |
PageRank | References | Authors |
0.66 | 38 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhang Yulun | 1 | 206 | 22.15 |
| Tian Yapeng | 2 | 147 | 9.54 |
| Yu Kong | 3 | 412 | 24.72 |
| Bineng Zhong | 4 | 245 | 20.13 |
| Yun Fu | 5 | 4267 | 208.09 |