# DDIM

A UAV ISAC synthetic aperture radar imager casts imaging as an exact linear inverse problem, using a pretrained diffusion model as prior and the echoes as likelihood guidance to reconstruct super-resolution images under arbitrary known trajectories and unknown position errors.

A UAV ISAC synthetic aperture radar imager casts imaging as an exact linear inverse problem, using a pretrained diffusion model as prior and the echoes as likelihood guidance to reconstruct super-resolution images under arbitrary known trajectories and unknown position errors.

Provenance watermarks for diffusion-generated images were previously suppressed by removal attacks with little visible quality loss. WaveMark embeds the message in the initial noise's wavelet domain, using a key to spread replicas of each message bit across spatial positions, wavelet orientations, and scales, then encodes each bit in a carrier's projection magnitude. On SD2.1 it reaches 100.0% bit accuracy without attacks and retains 88.66% and 88.06% under MarkNull and MarkNull-A, above the compared baselines.

Provenance watermarks for diffusion-generated images were previously suppressed by removal attacks with little visible quality loss. WaveMark embeds the message in the initial noise's wavelet domain, using a key to spread replicas of each message bit across spatial positions, wavelet orientations, and scales, then encodes each bit in a carrier's projection magnitude. On SD2.1 it reaches 100.0% bit accuracy without attacks and retains 88.66% and 88.06% under MarkNull and MarkNull-A, above the compared baselines.

The work proposes GSL-Diffusion, a time-series-conditioned diffusion channel estimator that denoises in the angular domain: a bidirectional LSTM encodes a short sequence of least-squares observations and injects global and sequential conditions into a 3D U-Net via multi-scale cross-attention, while a learnable SNR-gated late-fusion shortcut adaptively trades observation fidelity against the generative prior, and SNR-adaptive truncated DDIM inference reduces reverse steps; on QuaDRiGa/3GPP 38.901 time-varying channels it achieves the best NMSE almost across roughly -10 to 20 dB versus LS, LMMSE and diffusion baselines, often reaching one reverse step at high SNR.

The work proposes GSL-Diffusion, a time-series-conditioned diffusion channel estimator that denoises in the angular domain: a bidirectional LSTM encodes a short sequence of least-squares observations and injects global and sequential conditions into a 3D U-Net via multi-scale cross-attention, while a learnable SNR-gated late-fusion shortcut adaptively trades observation fidelity against the generative prior, and SNR-adaptive truncated DDIM inference reduces reverse steps; on QuaDRiGa/3GPP 38.901 time-varying channels it achieves the best NMSE almost across roughly -10 to 20 dB versus LS, LMMSE and diffusion baselines, often reaching one reverse step at high SNR.