LUCYD: A Feature-Driven Richardson-Lucy Deconvolution Network

TL;DR

LUCYD is a novel deep learning-based deconvolution method that combines Richardson-Lucy principles with feature fusion to enhance volumetric microscopy images, outperforming existing techniques in quality and generalizability.

Contribution

The paper introduces LUCYD, a feature-driven Richardson-Lucy deconvolution network that integrates image formation modeling with deep feature fusion for improved microscopy image restoration.

Findings

LUCYD outperforms state-of-the-art methods in synthetic and real microscopy images.

The model enhances resolution, contrast, and overall image quality.

It effectively handles various microscopy modalities and imaging conditions.

Abstract

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.