Name
Abstract | ||
|---|---|---|
Although sparse coding error has been introduced to improve the performance of sparse representation-based image denoising, however, the sparse coding noise is not tight enough. To suppress the sparse coding noise, we exploit a couple of images to estimate unknown sparse code. There are two main contributions in this paper: The first is to use a reference denoised image and an intermediate denoised image to estimate the sparse coding coefficients of the original image. The second is that we set a threshold to rule out blocks of low similarity to improve the accuracy of estimation. Our experimental results have shown improvements over several state-of-the-art denoising methods on a collection of 12 generic natural images. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1007/978-3-319-73600-6_1 | Lecture Notes in Computer Science |
Keywords | Field | DocType |
image denoising,Sparse representation model,Sparse coding coefficients,Sparse coding noise,Noise level | Noise reduction,Computer vision,Pattern recognition,Computer science,Neural coding,Sparse approximation,Noise level,Image denoising,Artificial intelligence | Conference |
Volume | ISSN | ISBN |
10705 | 0302-9743 | 9783319735993 |
Citations | PageRank | References |
0 | 0.34 | 14 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kai Lin | 1 | 365 | 46.37 |
| Ge Li | 2 | 112 | 29.37 |
| Yiwei Zhang | 3 | 52 | 12.65 |
| Zhong Jiaxing | 4 | 0 | 1.35 |