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.
翻译:近些年来,神经网络基于图像脱色的方法使生物医学显微镜数据的分析发生了革命性的变化。 自我监督的方法,如Nise2Void(N2V),即使没有专门的培训数据,也几乎适用于所有噪音的数据集。 可以说,这有助于在整个生命科学中迅速和广泛采用N2V。 不幸的是, N2V的盲点培训可以导致相当可见的检查板工艺品,从而大大降低了最后预测的质量。 在这项工作中,我们提出了两种修改,即香草N2V的设置,既有助于大量减少不需要的文物。 首先,我们建议修改网络结构,即在整个使用的U-Net中使用Blullerpool而不是MaxPool层,将剩余U-Net转回到一个非重复的U-Net,并消除最上端U-Net的跳过连接。 此外,我们提出了新的替换战略,以确定在选举的盲人像素中填充的像素密度值。 我们从合成的、多级的变压型的图像中,从我们所增加的合成的图像中校验了各种微变压的自我图像。