CDNet converts coupled dictionary learning's unique-common prior into a joint unfolding architecture with block-sparse interaction and a high-low frequency fidelity loss, delivering competitive fusion performance at lower compute cost across four image fusion tasks.
Image fusion in the loop of high-level vision tasks: A semantic-aware real-time infrared and visible image fusion network,
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
A conditional U-Net with weather conditioning at the bottleneck plus pre- and post-processing translates aerial RGB to thermal images, reaching PSNR 14.55, SSIM 0.81, LPIPS 0.17 and outperforming the ThermalGen baseline on a held-out test set.
citing papers explorer
-
Combined Dictionary Unfolding Network with Gradient-Adaptive Fidelity for Transferable Multi-Source Fusion
CDNet converts coupled dictionary learning's unique-common prior into a joint unfolding architecture with block-sparse interaction and a high-low frequency fidelity loss, delivering competitive fusion performance at lower compute cost across four image fusion tasks.
-
A Conditional U-Net Pipeline with Pre- and Post-Processing for Aerial RGB-to-Thermal Image Translation
A conditional U-Net with weather conditioning at the bottleneck plus pre- and post-processing translates aerial RGB to thermal images, reaching PSNR 14.55, SSIM 0.81, LPIPS 0.17 and outperforming the ThermalGen baseline on a held-out test set.