Name
Abstract | ||
|---|---|---|
The state of the art deep learning based denoising methods can achieve great denoising results. However, due to the lack of clean training data, the ground truth and noise level are unknown, traditional denoising methods are difficult to remove blind noise in general images. Furthermore, deep learning methods require specific noise levels to train the model, and specific models are built only deal with one noise level. In this paper, we propose a Nonsubsampled Countourlet Transform based convolutional network model (CTCNN) to deal with Gaussian noise and the noise of real images. The model is modified by U-Net, nonsubsampled Countourlet Transform (NSCT) and inverse NSCT are used to instead of sum pooling layer and up-convolution operation. NSCT can decrease the size of feature maps and preserve details of images without information loss. Different training strategies are adopted to train models in order to handle blinding noise such as underwater images which contain noise naturally. Simulation results show the proposed method is effective in standard images dataset and blind noisy images. The model we proposed has been compared with other state of the art denoising methods, and better subjective representation and PSNR improvement are obtained. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1016/j.image.2019.115727 | Signal Processing: Image Communication |
Keywords | Field | DocType |
Nonsubsampled countourlet transform,Convolutional Neural Networks,Image denoising,Gaussian noise | Noise reduction,Computer vision,Inverse,Computer science,Noise level,Pooling,Ground truth,Artificial intelligence,Deep learning,Real image,Network model | Journal |
Volume | ISSN | Citations |
82 | 0923-5965 | 1 |
PageRank | References | Authors |
0.35 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhiyu Lyu | 1 | 1 | 0.35 |
| Chengkun Zhang | 2 | 3 | 2.40 |
| Min Han | 3 | 761 | 68.01 |