  Inverse problems in image reconstruction are fundamentally complicated by
unknown noise properties. Classical iterative deconvolution approaches amplify
noise and require careful parameter selection for an optimal trade-off between
sharpness and grain. Deep learning methods allow for flexible parametrization
of the noise and learning its properties directly from the data. Recently,
self-supervised blind-spot neural networks were successfully adopted for image
deconvolution by including a known point-spread function in the end-to-end
training. However, their practical application has been limited to 2D images in
the biomedical domain because it implies large kernels that are poorly
optimized. We tackle this problem with Fast Fourier Transform convolutions that
provide training speed-up in 3D microscopy deconvolution tasks. Further, we
propose to adopt a Siamese invariance loss for deconvolution and empirically
identify its optimal position in the neural network between blind-spot and full
image branches. The experimental results show that our improved framework
outperforms the previous state-of-the-art deconvolution methods with a known
point spread function.
