Traditional visual Mamba (VIM) methods directly flatten the two-dimensional spatial image into a one-dimensional plane, which can capture long-distance dependencies, but also disrupt the local spatial structure of neighboring pixels in the original two-dimensional plane, thereby failing to capture local details. To address this, we introduce the panoramic state-space lightweight super-resolution model (PMambaIR) and propose the residual panoramic spatial group as the core building block. The residual panoramic spatial group component mainly includes two innovative modules. Specifically, we first introduce a new cascaded scanning strategy that promotes the interaction between local information, cross-scale information, and global information, effectively capturing local information while preserving global dependencies, thereby achieving panoramic feature extraction. Secondly, we propose a hybrid state-space block, which can simultaneously model pixel information from both spatial and channel dimensions, limiting the influence of irrelevant features on the model, thereby exploiting the potential relevance of channel and spatial domain information. The PSNR of PMambaIR outperforms existing models by an average of 0.11 dB on benchmark datasets such as Set14 and Urban100. Objective quantitative and qualitative analyses indicate that this method achieves high PSNR and SSIM, while subjective experimental results demonstrate rich details and visual effects.