Toward Real-world Infrared Image Super-Resolution: A Unified Autoregressive Framework and Benchmark Dataset

Yang Zou; Jun Ma; Zhidong Jiao; Xingyuan Li; Zhiying Jiang; Jinyuan Liu

cs.CVMar 5, 2026

Toward Real-world Infrared Image Super-Resolution: A Unified Autoregressive Framework and Benchmark Dataset

Authors: Yang Zou, Jun Ma, Zhidong Jiao, Xingyuan Li, Zhiying Jiang, Jinyuan Liu

Organizations: School of Computer Science, Northwestern Polytechnical University · School of Software Technology & DUT-RU International School of ISE, Dalian University of Technology · School of Computer Science, Zhejiang University · College of Information Science and Technology, Dalian Maritime University

Abstract

Infrared image super-resolution (IISR) under real-world conditions is a practically significant yet rarely addressed task. Pioneering works are often trained and evaluated on simulated datasets or neglect the intrinsic differences between infrared and visible imaging. In practice, however, real infrared images are affected by coupled optical and sensing degradations that jointly deteriorate both structural sharpness and thermal fidelity. To address these challenges, we propose Real-IISR, a unified autoregressive framework for real-world IISR that progressively reconstructs fine-grained thermal structures and clear backgrounds in a scale-by-scale manner via thermal-structural guided visual autoregression. Specifically, a Thermal-Structural Guidance module encodes thermal priors to mitigate the mismatch between thermal radiation and structural edges. Since non-uniform degradations typically induce quantization bias, Real-IISR adopts a Condition-Adaptive Codebook that dynamically modulates discrete representations based on degradation-aware thermal priors. Also, a Thermal Order Consistency Loss enforces a monotonic relation between temperature and pixel intensity, ensuring relative brightness order rather than absolute values to maintain physical consistency under spatial misalignment and thermal drift. We build FLIR-IISR, a real-world IISR dataset with paired LR-HR infrared images acquired via automated focus variation and motion-induced blur. Extensive experiments demonstrate the promising performance of Real-IISR, providing a unified foundation for real-world IISR and benchmarking. The dataset and code are available at: https://github.com/JZD151/Real-IISR.

Figures & tables

Figure 2: Overview of Real-IISR. The Thermal-Structural Guidance (TSG) module fuses thermal priors for degradation-aware encoding. The VAR backbone performs scale-by-scale generation via next-scale prediction, while the Condition-Adaptive Codebook (CAC) dynamically adjusts quantized embeddings based on degradation-aware priors for thermal fidelity. Finally, the Thermal Order Consistency Loss $\mathcal{L}_{\text{TOC}}$ preserves physically consistent thermal ordering.

Figure 3: Data collection pipeline of FLIR-IISR.

Datasets			FLIR-IISR@Set5		FLIR-IISR@Set15		$\text{M}^{3}\text{FD}$ @Set5		$\text{M}^{3}\text{FD}$ @Set15
Methods			MUSIQ $\uparrow$	MANIQA $\uparrow$	MUSIQ $\uparrow$	MANIQA $\uparrow$	MUSIQ $\uparrow$	MANIQA $\uparrow$	MUSIQ $\uparrow$	MANIQA $\uparrow$
Low Resolution	-	-	27.9188	0.2030	25.4302	0.1740	24.2688	0.1438	24.7427	0.1770
High Resolution	-	-	55.1375	0.3333	51.8250	0.3036	22.6813	0.1626	28.7760	0.2149
HAT [ 2 ]	CVPR’23	ISR	40.9844	0.2843	33.9990	0.2349	22.2281	0.1822	25.5260	0.2351
BI-DiffSR [ 3 ]	NIPS’24	ISR	37.6250	0.2279	32.5854	0.1883	19.8031	0.1354	25.7417	0.1755
PFT-SR [ 22 ]	CVPR’25	ISR	41.1875	0.2847	33.9823	0.2366	22.3563	0.1769	25.7583	0.2393

Table 1: No-reference Metrics Comparison on FLIR-IISR and

\text{M}^{3}\text{FD}

datasets. The best is in bold , while the second is underlined . For

\text{M}^{3}\text{FD}

, Set5/15 are randomly sampled subsets. For FLIR-IISR, Set5/15 corresponds to motion/optical blur.

Datasets			FLIR-IISR@Set5			FLIR-IISR@Set15			$\text{M}^{3}\text{FD}$ @Set5			$\text{M}^{3}\text{FD}$ @Set15
Methods			PSNR $\uparrow$	SSIM $\uparrow$	LPIPS $\downarrow$	PSNR $\uparrow$	SSIM $\uparrow$	LPIPS $\downarrow$	PSNR $\uparrow$	SSIM $\uparrow$	LPIPS $\downarrow$	PSNR $\uparrow$	SSIM $\uparrow$	LPIPS $\downarrow$
BI-DiffSR [ 3 ]	NIPS’24	ISR	27.1976	0.7869	0.4218	28.7676	0.8049	0.5206	35.9204	0.9062	0.3235	34.8666	0.8644	0.3133
DifIISR [ 14 ]	CVPR’25	IISR	27.1969	0.8195	0.2525	28.5603	0.8474	0.2739	35.8423	0.9318	0.2474	34.6620	0.9114	0.2214
SinSR [ 42 ]	CVPR’24	R-ISR	26.7594	0.6970	0.3670	28.3163	0.8521	0.2956	35.1806	0.9323	0.2528	34.0048	0.9041	0.1652
VARSR [ 31 ]	ICML’25	R-ISR	26.9767	0.7868	0.2304	28.3439	0.8613	0.2003	33.6762	0.9268	0.2436	32.6001	0.8997	0.1895
Ours	-	R-IISR	28.5126	0.8278	0.1615	29.5136	0.8895	0.1340	32.3175	0.9383	0.1997	31.5633	0.9047	0.1361

Table 2: Reference-based Metrics Comparison on FLIR-IISR and

\text{M}^{3}\text{FD}

datasets. The best is in bold , while the second is underlined . For

\text{M}^{3}\text{FD}

, Set5/15 are randomly sampled subsets. For FLIR-IISR, Set5/15 corresponds to motion/optical blur.

Figure 4: Efficiency comparison in terms of perceptual MUSIQ and FPS; circle diameter indicates model parameters.

Figure 5: Qualitative comparison of IISR with SOTA methods on FLIR-IISR and $\text{M}^{3}\text{FD}$ datasets. The graph illustrates grayscale fluctuations along the blue -marked sampling line, and red -marked sampling line denotes the HR.

Figure 6: Qualitative ablation on the Thermal-Structural Guidance (TSG) and Condition-Adaptive Codebook (CAC).

Figure 7: Quantitative ablation of TSG, CAC, and $\mathcal{L}_{\text{TOC}}$ on PSNR , SSIM , and MUSIQ .

Figure 8: Qualitative ablation on the Thermal Order Consistency Loss $\mathcal{L}_{\text{TOC}}$ with graph illustrates grayscale fluctuations along HR , LR , and models .

Figure 9: Quantitative ablation on the baseline choice of diffusion and VAR .

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Infrared image super-resolution (IISR) mitigates the limitations imposed by low spatial resolution. Existing methods have recognized that IISR should preserve consistency in global distribution and structural information while enhancing image clarity. However, these methods are either insufficient or overly intrusive, a problem that becomes even more pronounced in diffusion-based models. To address these issues, we propose a dual-path diffusion-based framework for IISR, termed Shift-IISR. The proposed method is designed to improve the consistency of IISR results while preserving the generative capacity of diffusion models. Specifically, we develop a Global Representation Modulation (GRM) module to extract modality-specific information from infrared imagery and guide the global distribution of the diffusion model toward the ground truth. In addition, we introduce a Local Structure Refinement (LSR) module to encourage the model to focus on structural information at each step of the iterative denoising process. Extensive experiments demonstrate that the proposed method effectively improves distributional and structural consistency while maintaining competitive super-resolution performance. The source code of the proposed Shift-IISR can be available at https://github.com/Assassink8/Shift-IISR.

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Oct 5, 2026

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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.

Mengyuan Li, Changhong Fu, Jun Zhang +3

School of Mechanical Engineering, Tongji University, Shanghai 201804, China

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