Multi-View Single-Scan Visual State-Space Network for Efficient Image Super-Resolution

Single image super-resolution (SISR) seeks to reconstruct high-resolution images from low-resolution inputs under the tight latency and memory budgets of edge devices. Recent visual state space models enable linear-time sequence modeling, but they often approximate two-dimensional dependencies through multiple directional scans, which increases computation and memory. We propose a Multi-View Single-Scan Visual State Space Network (MVSSN) for efficient image super-resolution. MVSSN contains three main components. A shuffled input stacking module (SISM) organizes replicated and shuffled input channels before a lightweight projection, where the learned projection filters help produce less redundant shallow responses. A multi-view single-scan block (MSSB) uses an alternating scan axis and invertible geometric transforms to change the serialization order observed by one selective scan in each block. Across stacked blocks, this provides a lightweight cross-layer approximation to multi-view context modeling. A multi-scale local feature block (MLFB) complements global aggregation with depthwise convolutions of complementary receptive fields and a compact MLP to restore local details. Experiments on standard SISR benchmarks show that MVSSN achieves competitive or better PSNR and SSIM with fewer than one million parameters and low FLOPs. Additional ablations, RealSR evaluations, and downstream detection and segmentation studies further examine its efficiency and practical behavior. We also discuss the limitation under unknown real degradation, where bicubic-trained models may still suffer from domain gaps.