Fluorescence microscopy is widely used in neuroscience to visualize cellular structures. However, commercial systems are usually voluminous, making them impractical for small animals such as rats. Although miniature microscopes like the Miniscope V4 (Zhang, 2018) allow in vivo neuronal recordings, the images obtained have aberrations and require heavy post-processing. This work aims to develop a miniature fluorescence microscope with improved optical quality and the possibility of super-resolution imaging through SUPPOSe (Toscani, 2019; Toscani, 2023; Martínez, 2024), addressing one of the main limitations of current devices. The system is built using smartphone camera lenses, compact and low-cost optics that provide high-quality imaging. It is based on a Raspberry Pi 3B+ with Python programming and the OMNIVISION OV5647 sensor (1.4 μm × 1.4 μm pixels). Initial results include 10-bit RAW acquisition, sensor characterization (linearity and noise), and focal length measurements of several lenses. With a telescope-type configuration (f2/f1), magnifications greater than one were obtained. Resin 3D printing was used to fabricate precise parts and miniaturize the system. Next steps include incorporating fluorescence to acquire images of fluorophores and applying image processing with SUPPOSe. This ongoing development aims to provide a portable and accessible tool for neuroscience, combining high-quality imaging with low cost and simple implementation.