  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
