Non-Local N2V: Improving N2V Networks for Spatially Correlated Noise

Blind Spot Networks (BSNs) are powerful deep denoisers that can be trained in a self-supervised way, namely without access to clean images. The training scheme of BSN-based methods builds upon the assumption that noise is pixel-wise independent and zero-mean. However, this assumption is not satisfied in many real-world scenarios, where the noise exhibits spatial correlation that degrades BSN denoising performance. Training deep neural networks under spatially correlated noise has attracted a lot of interest. However, several approaches require complex network architectures or resource-demanding training procedures, resulting in less practical solutions than simple BSNs. In this work, we present Non-Local N2V (NL-N2V), an extension of the mainstream Noise2Void (N2V) method. It is designed to address spatially correlated noise while maintaining the simplicity and the network architecture of N2V. Our approach introduces a novel masking strategy inspired by the Non-Local Self-Similarity prior that masks small regions around blind spots and replaces these with values obtained from non-local similar patches. Tests performed on real-world datasets corrupted by correlated noise, such as SIDD and DND, show that NL-N2V considerably outperforms the traditional N2V.