Name
Abstract | ||
|---|---|---|
Image denoising is still a challenging problem in image processing. The authors propose a novel image denoising method based on a deep convolution neural network (DCNN). Different from other learning-based methods, the authors design a DCNN to achieve the noise image. Thus, the latent clear image can be achieved by separating the noise image from the contaminated image. At the training stage, the ... |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1049/iet-ipr.2017.0389 | IET Image Processing |
Keywords | Field | DocType |
convergence of numerical methods,feedforward neural nets,gradient methods,image denoising | Noise reduction,Computer vision,Pattern recognition,Convolutional neural network,Image processing,Image denoising,Artificial intelligence,Kernel (image processing),Mathematics | Journal |
Volume | Issue | ISSN |
12 | 4 | 1751-9659 |
Citations | PageRank | References |
4 | 0.44 | 28 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Fu Zhang | 1 | 210 | 25.38 |
| Nian Cai | 2 | 28 | 6.74 |
| Jixiu Wu | 3 | 6 | 1.21 |
| Guandong Cen | 4 | 4 | 1.80 |
| Han Wang | 5 | 33 | 15.10 |
| Xin-Du Chen | 6 | 12 | 3.69 |