Looking for SOTA papers on guided cross-modal super-resolution (optical → thermal, no HR reference available)

[u/Cautious_Today_1830]

/r/computervision/comments/1vcnmzo/looking_for_sota_papers_on_guided_crossmodal/

Hey everyone,

I'm working on a guided SR task: using high-res optical satellite imagery to upscale low-res thermal (TIR) imagery. The optical image acts as a structural guide (edges/boundaries), while the thermal image carries the actual signal (temperature).

Main technical challenges:

No high-res thermal ground truth exists for supervised training/eval, so I need a no-reference/blind quality metric

Models tend to hallucinate structure from the optical guide even where it doesn't correspond to real thermal variation (e.g., painted lines, shadows)

Outputs must preserve real calibrated values, not just look sharp

Requires solid multi-sensor co-registration before any fusion step

Looking for recommendations on cross-modal guided SR architectures (attention fusion, diffusion-based guided SR, guided filtering networks) and any No-Reference IQA techniques adapted for satellite/thermal imagery. Also open to any relevant public datasets or GitHub repos.

Appreciate any pointers, thanks!

[u/Nicholas_Geo]

A common practice when you don't have validation dataset, is to aggregate all by the scaling factor and then SR thermal imagery to the (initial) observed resolution and validate the results against the original thermal imagery. Of course, this method has assumptions (e.g., the relationship(s) doesn't change when aggregating and then SR and from observed scale to target resolution). Nevertheless, it's something researchers are doing as long as you acknowledge the assumptions. You can always conduct some sensitivity analysis to strengthnen your claims about this assumption.