상세정보
학술지
DVSNet: Deep Variance-stabilised Network Robust to Spatially Variant Characteristics in Imaging
- 발행일
- 201905
- 출처
- Electronics Letters, v.55 no.9, pp.529-531
- ISSN
- 0013-5194
- 출판사
- IET
- 협약과제
- 19MH1500, 5G 기반의 스마트시티 서비스 개발 및 실증, 한미경
- 초록
- 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 제안 키워드
- 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