In recent years, neural network based image denoising approaches have
revolutionized the analysis of biomedical microscopy data. Self-supervised
methods, such as Noise2Void (N2V), are applicable to virtually all noisy
datasets, even without dedicated training data being available. Arguably, this
facilitated the fast and widespread adoption of N2V throughout the life
sciences. Unfortunately, the blind-spot training underlying N2V can lead to
rather visible checkerboard artifacts, thereby reducing the quality of final
predictions considerably. In this work, we present two modifications to the
vanilla N2V setup that both help to reduce the unwanted artifacts considerably.
Firstly, we propose a modified network architecture, i.e., using BlurPool
instead of MaxPool layers throughout the used U-Net, rolling back the residual
U-Net to a non-residual U-Net, and eliminating the skip connections at the
uppermost U-Net level. Additionally, we propose new replacement strategies to
determine the pixel intensity values that fill in the elected blind-spot
pixels. We validate our modifications on a range of microscopy and natural
image data. Based on added synthetic noise from multiple noise types and at
varying amplitudes, we show that both proposed modifications push the current
state-of-the-art for fully self-supervised image denoising.

本研究提出了两种改进的方法来减少 N2V 中产生的不良伪影，并通过测试表明，这两种方法可以推进图像去噪领域的全面自我监督技术的发展。