W2S: Microscopy Data with Joint Denoising and Super-Resolution for Widefield to SIM Mapping

TL;DR

This paper introduces a new microscopy dataset for joint denoising and super-resolution, benchmarks existing methods, and shows that end-to-end training outperforms sequential approaches in microscopy image enhancement.

Contribution

The paper presents the W2S dataset for benchmarking joint denoising and super-resolution in microscopy, and demonstrates the superiority of end-to-end trained networks over sequential methods.

Findings

State-of-the-art SR networks perform poorly on noisy inputs.

Sequential denoising followed by SR does not always yield optimal results.

End-to-end trained JDSR networks outperform separate denoising and SR methods.

Abstract

In fluorescence microscopy live-cell imaging, there is a critical trade-off between the signal-to-noise ratio and spatial resolution on one side, and the integrity of the biological sample on the other side. To obtain clean high-resolution (HR) images, one can either use microscopy techniques, such as structured-illumination microscopy (SIM), or apply denoising and super-resolution (SR) algorithms. However, the former option requires multiple shots that can damage the samples, and although efficient deep learning based algorithms exist for the latter option, no benchmark exists to evaluate these algorithms on the joint denoising and SR (JDSR) tasks. To study JDSR on microscopy data, we propose such a novel JDSR dataset, Widefield2SIM (W2S), acquired using a conventional fluorescence widefield and SIM imaging. W2S includes 144,000 real fluorescence microscopy images, resulting in a total of 360 sets of images. A set is comprised of noisy low-resolution (LR) widefield images with different noise levels, a noise-free LR image, and a corresponding high-quality HR SIM image. W2S allows us to benchmark the combinations of 6 denoising methods and 6 SR methods. We show that state-of-the-art SR networks perform very poorly on noisy inputs. Our evaluation also reveals that applying the best denoiser in terms of reconstruction error followed by the best SR method does not necessarily yield the best final result. Both quantitative and qualitative results show that SR networks are sensitive to noise and the sequential application of denoising and SR algorithms is sub-optimal. Lastly, we demonstrate that SR networks retrained end-to-end for JDSR outperform any combination of state-of-the-art deep denoising and SR networks