DVSNet: deep variance‐stabilised network robust to spatially variant characteristics in imaging
Seok Bong Yoo, Mingye Han · Electronics Letters · 2019
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.