LUCYD: A Feature-Driven Richardson-Lucy Deconvolution Network
Tomáš Chobola Gesine Müller Veit Dausmann Anton Theileis Jan Taucher Jan Huisken Tingying Peng
The process of acquiring microscopic images in life sciences often results in
image degradation and corruption, characterised by the presence of noise and
blur, which poses significant challenges in accurately analysing and
interpreting the obtained data. This paper proposes LUCYD, a novel method for
the restoration of volumetric microscopy images that combines the
Richardson-Lucy deconvolution formula and the fusion of deep features obtained
by a fully convolutional network. By integrating the image formation process
into a feature-driven restoration model, the proposed approach aims to enhance
the quality of the restored images whilst reducing computational costs and
maintaining a high degree of interpretability. Our results demonstrate that
LUCYD outperforms the state-of-the-art methods in both synthetic and real
microscopy images, achieving superior performance in terms of image quality and
generalisability. We show that the model can handle various microscopy
modalities and different imaging conditions by evaluating it on two different
microscopy datasets, including volumetric widefield and light-sheet microscopy.
Our experiments indicate that LUCYD can significantly improve resolution,
contrast, and overall quality of microscopy images. Therefore, it can be a
valuable tool for microscopy image restoration and can facilitate further
research in various microscopy applications. We made the source code for the
model accessible under https://github.com/ctom2/lucyd-deconvolution.