AOSNet-Sec: Aperture-orientation-spectrum Fusion with Statistical Markov Repair for Trustworthy Super-Resolution

Super-resolution (SR) is widely exploited in recognition pipelines where models are supposed to be lightweight and, increasingly, trustworthy under input perturbations and security threats. We address this need with two complementary contributions. First, we propose the Aperture Orientation Spectrum Fusion Network (AOSNet). AOSNet views reconstruction as multi-field fusion. Each AOS block combines an Aperture Pyramid Mixer (APM) for multi-scale receptive fields, an Orientation Selective Gate (OSG) for directional structure, and a Spectrum Subband Aggregator (SSA) for frequency refinement. Second, motivated by the growing demand for secure and reliable super-resolution in pattern recognition systems, we build a robustness-enhanced variant, AOSNet-Sec, by attaching a lightweight feature-space branch composed of a SR Stat Detector (SSD), a Security Detect Head (SDH), and a trainable SR Markov Repair (SMR). SSD aggregates per-channel and edge-contrast statistics to form a calibration-only security prior. Meanwhile, the trainable SMR iteratively updates suspicious feature maps guided by normalized statistics. Experiments on standard benchmarks show that AOSNet-Sec substantially reduces PSNR drop and Learned Perceptual Image Patch Similarity (LPIPS) degradation under strong white-box perturbations, which offers a practical trade-off between reconstruction quality and security-oriented robustness.