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DVSNet: deep variance‐stabilised network robust to spatially variant characteristics in imaging
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- Issue Date
- 2019-05
- Citation
- Electronics Letters, v.55, no.9, pp.529-531
- ISSN
- 0013-5194
- Publisher
- IET
- Language
- English
- Type
- Journal Article
- Abstract
- In real imaging systems, unwanted noises or artefacts can be caused by an image reconstruction error during photon-to-digital conversion. The generated noises typically tend to have spatially variant characteristics in an acquired image due to their signal dependencies. The noise characteristics via a preliminary study are analytically introduced and an image restoration scheme based on deep variance-stabilised network is proposed. Specifically, to improve the robustness of restoration performance to the noise properties, variance-stabilising transformation and binning priors are properly combined with a deep neural network as a layer structure particularly designed for denoising. Experimental results show the suggested network model outperforms existing state-of-the-art denoising methods based on convolutional neural network.
- KSP Keywords
- Convolution neural network(CNN), Deep neural network(DNN), Image reconstruction, Image restoration, Layer structure, Network model, Noise characteristics, Preliminary study, Reconstruction Error(RE), Spatially variant, denoising methods