TL;DR
The Multi-Scale Deformable Embedding and Dual-Guided Relation Network (MDGRN) combines fixed multi-scale perception with factorized deformable sampling to capture complementary local representations and further models channel compatibility and spatial reliability between adjacent feature stages.
A Spatio-Spectral Consistent Learning Network (SSCL-Net) built upon a lightweight backbone is proposed, which leverages directional pooling to precisely capture robust body structures while employing frequency-domain gating to explicitly suppress modality-specific textural interference.
Infrared imagery supports low-light target perception, but acquiring, registering, and annotating paired visible–infrared imagery is costly. Visible-to-infrared image translation can alleviate this demand, yet cross-modal differences often degrade object boundaries and spatial layouts, especially for small targets. We...
A Boundary-Guided Dual-Perspective Cross-Modal Fusion Network (BDPNet) is proposed to explicitly preserve shallow geometric structures and decouple deep semantic fusion into macroscopic and microscopic perspectives.
SSiffNet is proposed, a diffusion-based framework that explicitly enforces semantic alignment and structural consistency for aerial visible-to-infrared image translation and consistently outperforms existing state-of-the-art methods in both visual quality and quantitative metrics.
Visible-to-infrared (V2IR) image translation offers a practical approach to alleviating the high acquisition cost of infrared data and the scarcity of paired cross-modal samples. However, insufficient consideration of land-cover-dependent infrared responses can cause inaccurate target intensities, blurred boundaries, a...
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