Real-world thermal super-resolution (SR) is constrained by limited sensor resolution and the difficulty of obtaining corresponding high-resolution (HR) observations for direct model supervision. Conventional SR methods typically construct training pairs by treating captured thermal images with real-world degradations as HR references and applying predefined degradation to generate synthetic low-resolution (LR) inputs. Such a construction not only introduces a domain gap between synthetic and captured LR observations but also retains acquisition degradations in the supervision. To address this issue, we propose AstraSR, a real-world thermal SR method guided by GPT-6 Astra, a frontier multimodal generative model endowed with emergent and transformative visual capabilities. Specifically, we construct a dataset of image pairs by using captured LR thermal images to condition GPT-based HR reference. We develop a direct generative supervision strategy that learns from captured thermal inputs paired with GPT-generated HR references. Pixel, gradient, and perceptual losses jointly supervise the transfer of intensity patterns, structural boundaries, and visual details from the generated references. Qualitative comparisons with seven existing state-of-the-art real-world SR methods show continuous object contours, distinct structural boundaries, and smooth intensity transitions in the thermal scenes. These results demonstrate that AstraSR outperforms existing real-world SR methods in both thermal clarity and structural coherence.
Figures & tables
Fig. 1: Comparison of two thermal super-resolution paradigms. (a) The conventional paradigm constructs synthetic LR inputs by degrading captured thermal images that are used as HR references, causing a mismatch with real captured LR observations while retaining acquisition degradations in the supervision. (b) AstraSR leverages the emergent and transformative visual capabilities of GPT-6 Astra to construct structure-preserving HR references directly from captured thermal inputs, which are then used to supervise SR training for degradation-suppressed reconstruction. Enlarged regions show representative differences in boundary and structural recovery.
Fig. 2: Overview of the proposed AstraSR framework. GPT-6 Astra is employed only offline to construct structure-preserving HR references from captured thermal inputs under fidelity-constrained restoration instructions. These references supervise the learnable thermal SR model using pixel, gradient, and perceptual objectives. After training, the SR model is frozen and independently performs degradation-suppressed thermal SR, without requiring GPT-6 Astra during inference.
Fig. 3: Examples of captured thermal inputs and GPT-generated HR references. Panels (a) and (b) show a transmission tower with cables and vehicle windows with roof contours, respectively. Panels (c) and (d) show small pedestrian silhouettes and a window frame with a metal rack, respectively. These examples illustrate the contour and structural detail represented in the generated training references.
Fig. 4: Visual comparison of direct 4× thermal super-resolution. The red box in each full-scene view identifies the region enlarged. The compared SOTA methods includes Real-ESRGAN [ 9 ] , SwinIR [ 10 ] , FeMaSR [ 11 ] , ResShift [ 12 ] , SinSR [ 13 ] , PiSA-SR [ 14 ] , and AdcSR [ 15 ] . The LR patch provides input context, whereas the others show the corresponding enlarged outputs. AstraSR achieves the strongest visual restoration among the compared methods, producing structurally coherent details and well-defined boundaries while closely reproducing the restoration characteristics of the GPT-6 Astra-generated HR reference.
Method
Params (M)
FLOPs (G)
Real-ESRGAN [ 9 ]
16.70
587.43
SwinIR [ 10 ]
28.01
1004.98
FeMaSR [ 11 ]
34.06
766.34
ResShift [ 12 ]
173.92
6509.61
SinSR [ 13 ]
173.92
5293.94
PiSA-SR [ 14 ]
949.57
4435.43
TABLE I: Parameter and computational complexity. AstraSR achieves a favorable efficiency–complexity trade-off, requiring substantially fewer parameters and FLOPs than most recent real-world SR methods.
Bharti School of Telecommunications Technology and Management, Indian Institute of Technology Delhi, Delhi, India · School of Engineering, Jawaharlal Nehru University, Delhi, India