In this paper, we propose a Stochastic Primal-Dual Fixed Point (SPDFP) approach, leveraging to address the challenges of super-resolution reconstruction. Unlike traditional full-gradient methods, SPDFP employs stochastic gradients making it particularly well-suited for handling high-dimensional image data. Through numerical experiments, we demonstrate the superiority of our approach in enhancing resolution while preserving fine details, outperforming existing methods in both efficiency and reconstruction quality. Beyond empirical validation, we establish a solid theoretical framework, analyzing convergence properties under specific assumptions. This deeper insight into the method’s behavior highlights its practical effectiveness and sheds light on its potential and limitations in real-world super-resolution tasks.