Ultrasound Localization Microscopy (ULM) offers great advances compared to conventional ultrasound imaging, enabling the reconstruction of microvascular structures with micrometer-scale precision. Most model-based localization approaches for ULMrelyonasequentialfive-stepframework, which complicates parameter tuning and results in intricate tissue filtering. In addition, insufficient consideration of point spread function (PSF) also affects the final results. End-toend data-driven solutions offer promising results yet require training data and ground-truths. In this study, we introduced a novel approach, based on computational super-resolution referred to as BSRPCA- Blind Super-Resolved Robust Principal Component Analysis, to address these challenges. Our method replaces the first three steps of the ULM process with a single super-resolution step. Experimental results on both in silico and in vivo datasets have proven the effectiveness of our method compared to the other benchmarks.