Name
Abstract | ||
|---|---|---|
To reduce the data transmission pressure from the satellite to the ground, it is meaningful to process the image directly on the satellite. As the cornerstone of image processing, image denoising exceedingly improves the image quality to contribute to subsequent works. For on-orbit image denoising, we propose an end-to-end trainable image blind denoising network, namely IBDNet. Unlike existing image denoising methods, which either have a large number of parameters or are unable to perform image blind denoising, the proposed network is lightweight due to the residual bottleneck blocks as the main structure. Although our network does not use clean images for training, the experimental results on the public datasets indicate that the blindly denoised image quality of our method can be roughly the same as that of the state-of-the-art denoisers. Furthermore, we deploy the model (513 KB only) on the same equipment as the one on a satellite, which verifies the feasibility of running on the satellite. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1007/978-3-030-30508-6_4 | ARTIFICIAL NEURAL NETWORKS AND MACHINE LEARNING - ICANN 2019: IMAGE PROCESSING, PT III |
Keywords | DocType | Volume |
Image blind denoising, Lightweight, Residual bottleneck block | Conference | 11729 |
ISSN | Citations | PageRank |
0302-9743 | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ling Li | 1 | 49 | 5.85 |
| Junxing Hu | 2 | 0 | 1.69 |
| Yijun Lin | 3 | 0 | 1.69 |
| Fengge Wu | 4 | 0 | 0.34 |
| Junsuo Zhao | 5 | 0 | 2.37 |