Robust Signal Enhancement via Fractional Detail Views and Knowledge Guided Multi-view Fusion

Robust signal enhancement at extremely low SNR is fundamentally challenging because noise becomes strongly entangled with the signal and corrupts local time–frequency (TF) evidence. In this regime, fixed resolution short-time Fourier transform (STFT) enhancement with purely data driven convolutional biases can become overconfident in unreliable TF regions, causing unstable suppression or residual artifacts. We propose FracKGMF, which couples Fractional Distance Decay Convolution (FracConv) with Knowledge Guided Multi-view Fusion (KGMF) for expressive TF modeling and reliability aware decisions under heavy corruption. FracConv introduces a lightweight fractional distance decay family that reshapes local interactions into long tailed receptive patterns, enabling aggregation of weak but globally consistent cues when per-bin observations are ambiguous. KGMF further injects a Wiener inspired reliability view derived from noise statistics to calibrate multi-view fusion, avoiding over-suppression in uncertain regions while exploiting confident structure for effective denoising. Across speech and electromagnetic (EM) benchmarks, FracKGMF improves perceptual quality and intelligibility over state-of-the-art baselines, with especially robust gains at extremely low SNR; on EM dataset at -20 dB, it achieves an average improvement of 33 dB where conventional TF methods performed poorly. The code will be released upon acceptance.