1 Parameters

Introduction

In the user interface (UI), we included thirteen parameters to adapt to different hardware environments, experimental conditions, and fluorescence microscopes. To simplify the usage of this software, we have classified them into three categories: fixed parameters, image property parameters, and content-aware parameters.

The system hardware determines fixed parameters, and the image property parameters are associated with image quality, such as high-or-low SNR and strong-or-weak background. Ten parameters in these two categories are selected based on the system and the image property and need little tuning. The three parameters left belong to Content-aware parameters. They need to be adjusted carefully to achieve the optimal reconstruction results.

We introduced a four-step workflow to finetune these content-aware parameters on the next wiki page, which serves as a general guide.

Fixed parameters:

• Pixel size: The physically equivalent pixel size of the final images.
• Wavelength: The emission wavelength of the fluorescence probes.
• Effective numerical aperture: Effective NA of the images based on its spatial resolution.
• 3D imaging: This is the option to choose whether the input images are volumetric or not.
• GPU acceleration: This is the option to choose whether the calculation uses CUDA-GPU or not.

Image property parameters:

• Background: Based on the actual background fluorescence of the images processed, users can choose no background or the background under high-dose (HI) or low-dose of illumination (LI). Both the types HI and LI have weak and strong magnitude options. Thus five options of background are: No, Weak-HI, Strong-HI, Weak-LI, and Strong-LI.
• Upsampling: In conditions of inadequate Nyquist sampling, we manually up-sample images to achieve the theoretical resolution increase posed by the sparse deconvolution. We usually choose the spatial upsampling method for low-SNR images and the Fourier upsampling method for high-SNR ones.
• t (z)-axial continuity: This parameter is for adjusting the continuity along the input dataset's t or z-axis. We set the t (z)-axial continuity less than or equal to 1 to avoid temporal blurring. For the fast time-lapse imaging, while the fidelity is less than 100, the t (z)-axial continuity is usually assigned as one-hundredth of the fidelity. If the object being imaged has undergone fast movements, we need to set this parameter to a small number (0.1) or even zero to avoid causing motion artifacts.
• Sparse iteration times: Usually, we set it to 100 iterations. If spatial upsampling is used, we need to increase the number to 200 or 300 to ensure the sparse reconstruction convergence.
• Iterative deconvolution: We usually choose the Richardson-Lucy algorithm (RL) to deconvolve low-SNR images and the LandWeber deconvolution (LW) to deconvolve high-SNR ones.

Content-aware parameters:

• Image fidelity: This parameter denotes the distance between the image before and after the sparse reconstruction and is the inverse of the xy continuity. Usually, we use a large value (1000~300) for high SNR images.
• Sparsity: This parameter represents the relative sparsity constraint enforced on the reconstruction. Usually, we pre-set this value to one-tenth of the image fidelity term. However, an unnecessary high sparsity value may remove weak signals. Thus we need to finetune this parameter back-and-forth based on the final deconvolution result.
• Iterative deconvolution times: This parameter sets the times for the post iteration deconvolution. Because the LW method is slower than the vector extrapolation version of the RL method in reaching convergence, we choose 5-15 iteration times for the RL algorithm and 30-50 for the LW algorithm.

TAKE HOME messages:

• The ten parameters in the first two categories are primarily determined by the optical system and image property and need little tuning.
• Only the three content-aware parameters need to be adjusted back-and-forth carefully by visual examination of the reconstruction results.