Name
Abstract | ||
|---|---|---|
Image denoising is a common problem during image processing. Salt and pepper noise may contaminate an image by randomly converting some pixel values into 255 or 0. The traditional image denoising algorithm is based on filter design or interpolation algorithm. There exists no work using the convolutional neural network (CNN) to directly remove salt and pepper noise to the authors’ knowledge. In this study, they utilise CNN with the multi-layer structure for the removal of salt and pepper noise, which contains padding, batch normalisation and rectified linear unit. In training, they divide images into three parts: training set, validation set and test set. Experimental results demonstrate that the architecture can effectively remove salt and pepper noise for the various noisy images. In addition, their model can remove high-density noise well due to the extensive local receptive fields of the deep neural networks. Finally, extensive experimental results show that their denoiser is effective for those images with a large number of interference pixels which may cause misjudgement. In a word, they generalise the application of CNN to salt and pepper noise removal and obtain competitive results. |
Year | DOI | Keywords |
|---|---|---|
2019 | 10.1049/iet-ipr.2018.6004 | image denoising,convolutional neural nets,image resolution |
DocType | Volume | Issue |
Journal | 13 | 9 |
ISSN | Citations | PageRank |
1751-9659 | 1 | 0.35 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yan Xing | 1 | 43 | 15.82 |
| Jian Xu | 2 | 224 | 55.55 |
| Jieqing Tan | 3 | 130 | 28.88 |
| Daolun Li | 4 | 3 | 2.00 |
| Wen-shu Zha | 5 | 2 | 1.37 |