MM-BSN: Self-Supervised Image Denoising for Real-World with Multi-Mask based on Blind-Spot Network

Understand

Recent advances in deep learning have been pushing image denoising techniques to a new level.

• In self-supervised image denoising, blind-spot network (BSN) is one of the most common methods.
• However, most of the existing BSN algorithms use a dot-based central mask, which is recognized as inefficient for images with large-scale spatially correlated noise.
• In this paper, we give the definition of large-noise and propose a multi-mask strategy using multiple convolutional kernels masked in different shapes to further break the noise spatial correlation.