OpenLM: an open-source pixel super-resolution platform for lens-free microscopy with applications in bacterial growth...

Monitoring bacterial growth and detecting early-stage colony formation are essential tasks in biomedical research, clinical diagnostics, and food and water safety. However, conventional imaging systems for bacterial monitoring often require bulky optics, skilled operation, and high costs, making them unsuitable for scalable or field-deployable applications. Lens-free microscopy (LM) provides a promising alternative by enabling compact, low-cost imaging systems using only a light source and an image sensor, replacing the need for bulky objective lenses with computational algorithm. Still, a key limitation of LM is its resolution, which is fundamentally constrained by the sensor's pixel size. Pixel super-resolution techniques-especially when combined with multi-angle illumination using LED arrays-can significantly enhance resolution while maintaining a large field of view (FOV). We present OpenLM, an open-source lens-free microscopy platform integrated with a pixel super-resolution algorithm. The system is built from four affordable, off-the-shelf components: a Raspberry Pi camera, an optical filter, an LED array, and a Raspberry Pi board. Its 3D-printed housing enables easy replication and customization. User-friendly graphical interfaces for both Raspberry Pi OS and Windows provide camera control, real-time preview, image acquisition, and reconstruction-without requiring prior experience in lens-free imaging. To demonstrate its utility, we applied OpenLM to two bacterial imaging tasks: (1) long-term, time-lapse imaging of Escherichia coli (E. coli) colony growth, where colonies became visible within 30 minutes and complex spatial interactions emerged over time due to the wide FOV; and (2) early-stage colony detection using a YOLO (you only look once)-based deep learning model. With its affordability, high resolution, wide FOV, and ease of use, OpenLM is a practical and scalable tool for bacterial monitoring and other biomedical applications.