Angular Spectrum Propagation Modeling for Lightweight Image Dehazing

Most image dehazing methods operate in the pixel-intensity domain, leaving frequency-domain spectral distortions implicitly handled. We introduce Angular Spectrum Propagation Modeling (ASPM), a lightweight frequency-domain front-end inspired by the angular spectrum method in physical optics. ASPM treats a hazy image as a complex-valued virtual wave field and performs learned, per-channel spectral filtering via a propagation-distance representation and phase maps. Designed as an architecture-agnostic module within dehazing, ASPM provides a physics-inspired structured prior—not a claim of literal coherent-wave physics—that supports restoration even at minimal parameter scales. Under a matched-capacity comparison, replacing ASPM's constrained angular-spectrum operator with an unconstrained learnable complex filter of equal size still loses 1.30 dB, indicating the benefit comes from this specific structural constraint rather than merely from having a complex-valued frequency-domain component. Our resulting model, ASPMDehazeNet-S, is compact (0.04M parameters) and achieves 26.26 dB PSNR on SOTS-Indoor at 4.2 ms (over 230 FPS) on an NVIDIA RTX 4090. We evaluate on synthetic benchmarks (SOTS-Indoor, SOTS-Outdoor, RS-Haze) and two real-world benchmarks (NH-Haze, Dense-Haze): a scaled variant (ASPMDehazeNet-L, 0.1M) reaches 32.97 dB on SOTS-Indoor and is the best among the compared sub-1M models on SOTS-Indoor, SOTS-Outdoor, and both real-world benchmarks, while remaining competitive on high-resolution RS-Haze. Overall, ASPM offers a favorable accuracy–efficiency trade-off for lightweight dehazing.