Thanks to the advent of high-performance detector arrays, image scanning microscopy (ISM) has transformed the field of fluorescence laser-scanning microscopy1. In particular, the development of fast, sensitive single-photon avalanche diode (SPAD) arrays2 has paved the way for robust ISM imaging with exceptional information content3. In ISM, a focused excitation beam scans the sample. For each scan point, the SPAD array collects a small image — called a micro-image — of the diffraction-limited fluorescent region. In effect, each SPAD of the detector acts as an independent and displaced pinhole, generating a confocal-like image. Thus, the ISM dataset can be seen either as a large set of small wide-field images or as a small set of large confocal images, both point-of-views opening unique ways to process the data. Tailored algorithms enable the transformation of the raw ISM dataset into a single image with enhanced spatial resolution, signal-to-noise ratio, and optical sectioning. With the recent introduction into the market of commercial microscopes equipped with a detector array — namely, AiryScan from Zeiss, PRISM from Genoa Instruments, MATRIX from Abberior, and NSPARC from Nikon — we expect ISM to become increasingly widespread in use. With this in mind, we have designed an open-source tool called BrightEyes-ISM to provide the scientific community with a computational toolbox for processing the ISM datasets with cutting-edge algorithms for image reconstruction, image quality evaluation, and simulation. BrightEyes-ISM is an open-source Python package with three different modules, each containing functions specific to data loading, data analysis, or optical simulations. The simulation module contains a point spread function (PSF) simulator (Fig. 1a–c) and a tubulin simulator. The PSF simulator is currently built upon PyFocus4, a Python package that enables the vectorial calculation of focused optical fields. The simulator generates a set of PSFs, one for each element of the detector array, with all of the relevant physical parameters that can be fine-tuned. We also built a phase calculator that enables the introduction of any aberration or a user-defined phase mask. Optionally, the simulator can introduce a third field for stimulated emission depletion (STED), enabling the simulation of STED-ISM PSFs. The dataIO module enables the user to easily read and store data and metadata in a hierarchical data format (HDF), which is particularly convenient for ISM datasets that are inherently large and multi-dimensional (Fig. 1d). The analysis module contains libraries for state-of-the-art analysis of ISM data processing. The APR library contains the functions needed to perform adaptive pixel reassignment (APR), a fast and robust method to reconstruct a super-resolution image from an ISM dataset5 (Fig. 1e). The deconvolution library contains many functions to perform image deconvolution, the most important one being the multi-image deconvolution6 that enables an excellent alternative to APR for image reconstruction (Fig. 1f). The Focus-ISM library contains all of the functions needed to perform Focus-ISM, namely, an innovative algorithm that greatly enhances the optical sectioning of ISM, effectively removing the out-of-focus light from the ISM datasets7 (Fig. 1g). The FRC library provides the tools needed to perform Fourier ring correlation (FRC) analysis on the reconstruction, enabling a quantitative method to evaluate the resolution of the images8 (Fig. 1h). Notably, we also use FRC as a tool to estimate the PSFs from experimental data and perform blind multi-image deconvolution9. BrightEyes-ISM can be found on GitHub (https://github.com/VicidominiLab/BrightEyes-ISM), together with the complete documentation. Importantly, we intended our package to be useful both for developers and microscopy users. We therefore integrated the majority of the functionalities of BrightEyes-ISM into a Napari plugin, named Napari-ISM (https://github.com/VicidominiLab/napari-ISM). Napari is a multi-dimensional image viewer that provides a quick and easy way to explore and analyse the ISM data.