Name
Abstract | ||
|---|---|---|
A new denoising framework based on deep convolutional neural network for suppressing impulse noise in color images is proposed in this paper. The proposed framework consists of two modules: noise detection and image reconstruction, both of which are implemented by a deep convolutional neural network. First, a noise classifier network is trained to detect random-valued impulse noise in a color image, which not only can detect the noisy color vector pixels but also can further identify the corrupted channels of each noisy color pixel. Then, a sparse clean color image is computed by replacing the values of noisy channels with 0 and keeping other noise-free channels unchanged. Finally, the sparse clean color image is fed to another denoiser network to reconstruct the denoised image. Experimental results show that the proposed denoiser outperforms other state-of-the-art methods clearly in both performance measure and visual evaluation. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1016/j.asoc.2019.105558 | Applied Soft Computing |
Keywords | Field | DocType |
Color image,Impulse noise,Convolutional neural network | Noise reduction,Iterative reconstruction,Pattern recognition,Convolutional neural network,Communication channel,Artificial intelligence,Impulse noise,Pixel,Classifier (linguistics),Machine learning,Mathematics,Color image | Journal |
Volume | ISSN | Citations |
82 | 1568-4946 | 1 |
PageRank | References | Authors |
0.41 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wenhua Zhang | 1 | 4 | 1.89 |
| Lianghai Jin | 2 | 185 | 15.07 |
| Enmin Song | 3 | 176 | 24.53 |
| Xiangyang Xu | 4 | 76 | 10.40 |