Physics-Informed Spatially Variant Image Restoration for Unresolved Infrared Remote Sensing Small Targets

Abstract

Unresolved or nearly unresolved infrared remote sensing small targets captured by lightweight imaging systems are often severely degraded by field-dependent blur and noise, leading to energy dispersion, reduced local visibility, and degraded interpretability. This work proposes a physics-informed spatially variant image restoration method for this target regime. Unlike conventional restoration methods based on a spatially invariant degradation assumption, the proposed framework explicitly incorporates a spatially variant point spread function (PSF) prior into a learning-based restoration pipeline to model field-dependent imaging degradation. On this basis, a deep Wiener-style restoration mechanism is introduced to jointly recover target energy and suppress noise, while a matched-filter-inspired PSF-correlation-guided soft attention module is designed to emphasize target likelihood regions and mitigate clutter amplification during restoration. In addition, a target-oriented optimization strategy is adopted to promote local target observability for weak small targets under complex infrared backgrounds. By combining physical imaging priors with adaptive deep restoration, the proposed method offers an interpretable solution for spatially variant infrared small-target recovery when accurate local PSFs are available. On a controlled synthetic benchmark with calibrated PSF priors, the proposed method achieves a mean local signal-to-noise ratio (SNR) gain of 12.56 dB.