In traditional ultrasound (US) imaging, there has long been a trade-off between spatial resolution and imaging frame rate. Super-resolution (SR) imaging techniques stand out as extensively studied methods to overcome the spatial resolution limitation. Recently, these techniques have garnered significant attention in ultrafast US imaging research, enabling enhanced visualization of microvasculature. However, current SR methods encounter challenges, including the need to acquire a substantial number of image frames over an extended acquisition time, coupled with intricate post-processing steps. Convolutional neural networks (CNNs) emerge as promising approaches, surpassing classical model-based methods. Nevertheless, they face challenges arising from the lack of in vivo data and the absence of ground truth. In this work, we introduce a model-informed CNN for computing SR images, addressing generalization issues through an unpaired and unlabeled training approach. Our results are compared with those obtained from state-of-the-art techniques on both simulated and in vivo data.