A single-lens computational imaging system combines a single lens with post-processing algorithms to achieve a lightweight design while maintaining imaging quality. However, the computational inefficiency of existing reconstruction methods often limits the achievable frame rate on edge devices, falling short of the practical requirement of 30-60 frames per second (fps). Here, we adopt a physics-informed neural network that integrates an improved Wiener deconvolution (IWD) with a compact Res-Unet variant. The simple yet effective Wiener deconvolution step reduces image blur and spatially variant degradation, thereby alleviating the workload of the subsequent network and enabling high-quality, real-time reconstruction. Simulation and experimental results demonstrate that this framework can further reduce the algorithmic complexity for a single-lens system, achieving real-time reconstruction at 40 fps for 640×480 resolution on an RK3588 system-on-chip (SoC), while maintaining a system modulation transfer function (MTF) above 0.39 at Nyquist frequency (42 lp/mm).