Observing objects of small size has always been a charming pursuit of human beings.However, due to the physical phenomenon of diffraction, the optical resolution is restricted to approximately half the wavelength of light, which impedes the observation of subwavelength objects, typically smaller than 200 nm. This constrains its application in numerous scientific and industrial fields that aim to observe objects beyond the diffraction limit, such as native state coronavirus inspection.Fortunately, deep learning methods have shown remarkable potential in uncovering underlying patterns within data, promising to overcome the diffraction limit by revealing the mapping pattern between diffraction images and their corresponding ground truth object localization images. However, the absence of suitable datasets has hindered progress in this field - collecting high-quality optical data of subwavelength objects is very challenging as these objects are inherently invisible under conventional microscopy, making it impossible to perform standard visual calibration and drift correction. Therefore, in collaboration with top optical scientists, we provide the first general optical imaging dataset based on the "LEGO" concept for addressing the diffraction limit. Drawing an analogy to the modular construction of the LEGO blocks, we construct a comprehensive optical imaging dataset comprising subwavelength fundamental elements, i.e., small square units that can be assembled into larger and more complex objects of any shape. We then frame the task as an image-to-image translation task and evaluate various vision backbone methods. Experimental results validate our "LEGO" concept, demonstrating that models trained on basic square units can effectively generalize to realistic, more complex unseen objects. Most importantly, by highlighting this underexplored AI-for-science area and its potential, we aspire to advance optical science by fostering collaboration with the vision and machine learning communities.