pith. sign in

arxiv: 2605.17564 · v1 · pith:6Y2W6VYTnew · submitted 2026-05-17 · 💻 cs.CV

A Conditional U-Net Pipeline with Pre- and Post-Processing for Aerial RGB-to-Thermal Image Translation

Tseten Sherpa , Sikandar Ali , Shubham Parab , Haoyun Feng , Matthew Dennis , Keenan Gibbons , Verrah Otiende , Geoffrey H. Siwo This is my paper

Pith reviewed 2026-05-20 13:21 UTC · model grok-4.3

classification 💻 cs.CV
keywords aerial image translationRGB-to-thermalconditional U-Netweather conditioningthermal image generationPix2Piximage-to-image translation
0
0 comments X

The pith

A conditional U-Net supplied with weather data at its bottleneck layer produces more accurate thermal images from aerial RGB photos than prior generative models.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper aims to establish that a conditional U-Net, when given weather data at the bottleneck and paired with targeted pre- and post-processing inside a Pix2Pix setup, translates aerial RGB images into thermal images more effectively than existing approaches. A sympathetic reader would care because paired RGB-thermal data enables tasks such as image fusion, object tracking, and anomaly detection, yet such aligned pairs remain scarce, so reliable translation offers a route to generate thermal views from ordinary RGB cameras. The authors train on 612 paired images using 5-fold cross-validation and evaluate on held-out data, concluding that the weather conditioning contributes the largest gain among the modifications tested.

Core claim

The central claim is that a conditional U-Net architecture incorporating weather data at the bottleneck layer, together with saturation boost, contrast enhancement, and Gaussian blur steps, generates thermal images from aerial RGB inputs that better match real thermal captures than the ThermalGen baseline. Training proceeds on a set of 612 paired images with cross-validation, and the results indicate that the metadata conditioning serves as the primary driver of improved reconstruction by acting as a proxy for environmental conditions that shape thermal appearance.

What carries the argument

The conditional U-Net with weather data inserted at the bottleneck layer, which uses the metadata to modulate the generation of thermal output from RGB input within the Pix2Pix framework.

If this is right

  • Thermal image generation benefits when auxiliary metadata such as weather serves as a proxy for factors that affect temperature distributions across a scene.
  • Simple conditioning at the bottleneck can make a basic U-Net competitive with more complex generative models for aerial image translation tasks.
  • Targeted preprocessing for saturation and contrast plus post-processing blur can refine the fidelity of the translated thermal images.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • If weather conditioning works by capturing environmental context, the same strategy could support translation tasks in other modalities where external factors influence appearance, such as generating night-time or low-visibility images.
  • Wider adoption of this metadata approach might allow drone surveys to produce usable thermal data without carrying dedicated thermal sensors on every flight.

Load-bearing premise

Weather data fed at the bottleneck reliably stands in for the environmental conditions that determine how heat appears in the images, and the performance gains will hold for other image collections and different preprocessing choices.

What would settle it

Apply the conditional U-Net to a new collection of aerial RGB-thermal pairs captured under weather conditions absent from the original training set and check whether the outputs remain higher quality than the unconditioned baseline model.

Figures

Figures reproduced from arXiv: 2605.17564 by Geoffrey H. Siwo, Haoyun Feng, Keenan Gibbons, Matthew Dennis, Shubham Parab, Sikandar Ali, Tseten Sherpa, Verrah Otiende.

Figure 1
Figure 1. Figure 1: Variation within the urban dataset, including construction zones, parking lots, and large [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Excerpt from the dataset including the same venue in various rotations, included to [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: The conditional U-net Architecture [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: The conditional U-net Architecture the scene, where coarse environmental factors such as ambient temperature most directly influence thermal appearance. 2.4 Pix2Pix-based GAN with discriminator modifications As a comparison architecture shown in [PITH_FULL_IMAGE:figures/full_fig_p005_4.png] view at source ↗
read the original abstract

Paired RGB-thermal data has shown significant utility across a range of applications, including image fusion, object tracking, and anomaly detection; however, its broader adoption is constrained by the limited availability of aligned RGB-thermal image pairs. RGB-to-thermal (and vice versa) image translation has emerged as a practical solution to this challenge. Prior approaches including conditional generative adversarial networks (cGANs) such as ThermalGAN and Scalable Interpolant Transformer (SiT)-based architectures such as ThermalGen have demonstrated strong potential for aerial-to-thermal image translation. In this work, we explore alternative architectures that prioritize simplicity while maintaining performance. Specifically, we propose a conditional U-Net that incorporates weather data at the bottleneck layer, complemented by targeted preprocessing and post-processing techniques applied within the Pix2Pix GAN architecture. We utilize a training set of 612 paired RGB and thermal images, and evaluate over 5-fold cross-validation, ultimately testing on a held-out test set. Our conditional U-Net model performed best, with a peak signal-to-noise ratio (PSNR) of 14.5485, structural similarity index measure (SSIM) of 0.8095, and learned perceptual image patch similarity (LPIPS) of 0.1666. These results outperformed the base ThermalGen model, which attained PSNR, SSIM, and LPIPS scores of 7.56, 0.2444, and 0.6317 respectively. We find that while saturation boost and contrast enhancement for preprocessing and Gaussian blur for post-processing provide observable improvements, the incorporation of conditioning data was most effective. Our findings cement the potential of integrating auxiliary metadata into thermal image generation, suggesting that such information can serve as a proxy for environmental conditions critical to accurate thermal reconstruction.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 3 minor

Summary. The manuscript proposes a conditional U-Net for aerial RGB-to-thermal image translation that injects weather metadata at the bottleneck layer inside a Pix2Pix GAN pipeline, augmented by saturation/contrast preprocessing and Gaussian post-processing. On a 612-image paired dataset evaluated with 5-fold cross-validation plus a held-out test set, the authors report their model attaining PSNR 14.5485, SSIM 0.8095, and LPIPS 0.1666, outperforming a base ThermalGen model (PSNR 7.56, SSIM 0.2444, LPIPS 0.6317). They conclude that weather conditioning contributes the largest gain.

Significance. If the central performance claims and fair baseline comparisons hold, the work demonstrates that a relatively simple conditional U-Net can deliver competitive RGB-to-thermal translation results on aerial imagery while leveraging auxiliary metadata as a proxy for environmental conditions. This could offer a practical, lower-complexity alternative to cGAN or transformer-based methods in data-limited settings and supports broader use of weather or similar metadata for thermal reconstruction tasks.

major comments (2)
  1. Abstract: the outperformance claim over ThermalGen rests on metrics (PSNR 7.56, SSIM 0.2444, LPIPS 0.6317) whose provenance is unspecified. The text does not state that ThermalGen was re-implemented, retrained, and evaluated on the identical 612-image dataset, splits, weather metadata availability, saturation/contrast preprocessing, or Gaussian post-processing. If the cited numbers originate from the original ThermalGen publication on a different distribution, the large reported gap cannot be attributed to the bottleneck conditioning or U-Net architecture and directly undermines the central claim.
  2. Results section (and abstract statement that conditioning was most effective): no ablation table or quantitative breakdown isolates the incremental contribution of weather conditioning versus the chosen preprocessing and post-processing steps. Without such controlled comparisons, the assertion that conditioning provided the largest improvement lacks supporting evidence and weakens the key methodological conclusion.
minor comments (3)
  1. Abstract and evaluation description: the 5-fold cross-validation metrics are reported as single point values with no error bars, standard deviations, or statistical significance tests, making it difficult to assess the reliability of the reported improvements.
  2. Methods: a complete architecture diagram showing the precise location and format of weather-data injection at the U-Net bottleneck would improve clarity and reproducibility.
  3. Methods: the exact numerical levels chosen for saturation boost and contrast enhancement, as well as the Gaussian blur parameters, should be stated explicitly rather than described qualitatively.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive and detailed comments. We address each major comment point by point below, indicating where revisions will be made to improve clarity and support for our claims.

read point-by-point responses

  1. Referee: Abstract: the outperformance claim over ThermalGen rests on metrics (PSNR 7.56, SSIM 0.2444, LPIPS 0.6317) whose provenance is unspecified. The text does not state that ThermalGen was re-implemented, retrained, and evaluated on the identical 612-image dataset, splits, weather metadata availability, saturation/contrast preprocessing, or Gaussian post-processing. If the cited numbers originate from the original ThermalGen publication on a different distribution, the large reported gap cannot be attributed to the bottleneck conditioning or U-Net architecture and directly undermines the central claim.

    Authors: We acknowledge that the abstract does not explicitly describe the provenance of the ThermalGen baseline metrics. The numbers reported for ThermalGen were obtained by re-implementing and retraining the model from scratch on our exact 612-image paired dataset, using the same 5-fold cross-validation splits, held-out test set, and weather metadata availability. The same saturation/contrast preprocessing and Gaussian post-processing were applied to ensure a controlled comparison. To resolve any ambiguity, we will revise the abstract and add an explicit statement in the results section clarifying that ThermalGen was re-trained and evaluated under identical conditions on our data distribution. revision: yes

  2. Referee: Results section (and abstract statement that conditioning was most effective): no ablation table or quantitative breakdown isolates the incremental contribution of weather conditioning versus the chosen preprocessing and post-processing steps. Without such controlled comparisons, the assertion that conditioning provided the largest improvement lacks supporting evidence and weakens the key methodological conclusion.

    Authors: We agree that a quantitative ablation study is necessary to rigorously support the claim that weather conditioning contributed the largest improvement. Although our experimental process involved testing incremental component additions, the original manuscript did not include a dedicated ablation table. In the revised version, we will insert a new ablation table in the results section reporting PSNR, SSIM, and LPIPS for: (1) a plain U-Net baseline, (2) U-Net plus preprocessing only, (3) U-Net plus post-processing only, (4) U-Net plus weather conditioning only, and (5) the full proposed model. This will provide the requested controlled breakdown and strengthen the methodological conclusion. revision: yes

Circularity Check

0 steps flagged

No significant circularity in empirical pipeline or results

full rationale

The paper describes a conditional U-Net architecture with weather conditioning at the bottleneck, plus preprocessing and post-processing steps, trained on 612 paired images and evaluated via 5-fold cross-validation on a held-out test set. Reported metrics (PSNR, SSIM, LPIPS) are direct empirical measurements on unseen data and do not reduce by construction to any fitted parameters or self-defined quantities. No equations or derivations are presented that loop back to inputs; the comparison to ThermalGen cites prior external work without claiming a uniqueness theorem or self-citation chain that forces the result. The central claims rest on independent test-set performance rather than any of the enumerated circular patterns.

Axiom & Free-Parameter Ledger

2 free parameters · 2 axioms · 0 invented entities

The performance claims rest on the empirical training of a U-Net on 612 paired images, the assumption that weather metadata can stand in for thermal environmental factors, and the choice of particular preprocessing operations whose exact parameters are not detailed.

free parameters (2)
  • Weather conditioning injection point and format
    The decision to place weather data at the bottleneck and the encoding chosen for it are selected to improve reconstruction accuracy.
  • Saturation boost and contrast enhancement levels
    Specific numerical values for these preprocessing operations are tuned to yield observable visual improvements on the training data.
axioms (2)
  • domain assumption Paired RGB-thermal aerial data is scarce enough to justify synthetic generation
    Stated in the opening sentence as the constraint limiting broader adoption.
  • standard math U-Net with bottleneck conditioning is a suitable architecture for conditional image-to-image translation
    Invoked by building on the Pix2Pix GAN framework referenced in the abstract.

pith-pipeline@v0.9.0 · 5892 in / 1689 out tokens · 73982 ms · 2026-05-20T13:21:26.108816+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

What do these tags mean?
matches
The paper's claim is directly supported by a theorem in the formal canon.
supports
The theorem supports part of the paper's argument, but the paper may add assumptions or extra steps.
extends
The paper goes beyond the formal theorem; the theorem is a base layer rather than the whole result.
uses
The paper appears to rely on the theorem as machinery.
contradicts
The paper's claim conflicts with a theorem or certificate in the canon.
unclear
Pith found a possible connection, but the passage is too broad, indirect, or ambiguous to say the theorem truly supports the claim.

Reference graph

Works this paper leans on

21 extracted references · 21 canonical work pages · 1 internal anchor

  1. [1]

    RGB-T object tracking: Benchmark and baseline,

    C. Li, X. Liang, Y . Lu, N. Zhao, and J. Tang, “RGB-T object tracking: Benchmark and baseline,”Pattern Recognition, vol. 96, p. 106977, 2019

    work page 2019

  2. [2]

    LLVIP: A visible-infrared paired dataset for low-light vision,

    X. Jia, C. Zhu, M. Li, W. Tang, and W. Zhou, “LLVIP: A visible-infrared paired dataset for low-light vision,” inProc. IEEE/CVF Int. Conf. Comput. Vis. Workshops (ICCVW), 2021, pp. 3496–3504

    work page 2021

  3. [3]

    Multispectral pedestrian detection: Benchmark dataset and baseline,

    S. Hwang, J. Park, N. Kim, Y . Choi, and I. S. Kweon, “Multispectral pedestrian detection: Benchmark dataset and baseline,” inProc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), 2015, pp. 1037–1045

    work page 2015

  4. [4]

    Multispectral deep neural networks for pedestrian detection,

    J. Liu, S. Zhang, S. Wang, and D. N. Metaxas, “Multispectral deep neural networks for pedestrian detection,” inProc. British Machine Vision Conf. (BMVC), 2016

    work page 2016

  5. [5]

    ThermalGen: A scalable interpolant transformer for aerial RGB-to-thermal image translation,

    J. Xu, Y . Tang, J. Zhang,et al., “ThermalGen: A scalable interpolant transformer for aerial RGB-to-thermal image translation,” inAdv. Neural Inf. Process. Syst. (NeurIPS), 2025, arXiv:2509.24878

    work page arXiv 2025

  6. [6]

    Validation of GAN-based thermal infrared imagery synthesis from RGB images,

    X. Liu, Z. Wu, and X. Wang, “Validation of GAN-based thermal infrared imagery synthesis from RGB images,”IEEE Access, vol. 11, pp. 1–12, 2023

    work page 2023

  7. [7]

    ThermalDiffusion: Conditional denoising diffusion for visible-to-thermal translation,

    S. Lee, J. Kim, and H. Park, “ThermalDiffusion: Conditional denoising diffusion for visible-to-thermal translation,”IEEE Trans. Image Process., vol. 33, pp. 1–14, 2024

    work page 2024

  8. [8]

    Conditional Generative Adversarial Nets

    M. Mirza and S. Osindero, “Conditional generative adversarial nets,” arXiv:1411.1784, 2014

    work page internal anchor Pith review Pith/arXiv arXiv 2014

  9. [9]

    Photo-realistic single image super-resolution using a generative adversarial network,

    C. Ledig, L. Theis, F. Huszár,et al., “Photo-realistic single image super-resolution using a generative adversarial network,” inProc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), 2017, pp. 4681–4690

    work page 2017

  10. [10]

    Image-to-image translation with conditional adversarial networks,

    P. Isola, J.-Y . Zhu, T. Zhou, and A. A. Efros, “Image-to-image translation with conditional adversarial networks,” inProc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), 2017, pp. 1125–1134

    work page 2017

  11. [11]

    Unpaired image-to-image translation using cycle-consistent adversarial networks,

    J.-Y . Zhu, T. Park, P. Isola, and A. A. Efros, “Unpaired image-to-image translation using cycle-consistent adversarial networks,” inProc. IEEE Int. Conf. Comput. Vis. (ICCV), 2017, pp. 2223–2232

    work page 2017

  12. [12]

    Toward multimodal image-to-image translation,

    J.-Y . Zhu, R. Zhang, D. Pathak,et al., “Toward multimodal image-to-image translation,” inAdv. Neural Inf. Process. Syst., vol. 30, 2017

    work page 2017

  13. [13]

    ThermalGAN: Multimodal color-to-thermal image translation for person re-identification in multispectral dataset,

    V . V . Kniaz, V . A. Knyaz, J. Hlad˚ uvka, W. G. Kropatsch, and V . A. Mizginov, “ThermalGAN: Multimodal color-to-thermal image translation for person re-identification in multispectral dataset,” inProc. Eur . Conf. Comput. Vis. Workshops (ECCVW), 2018, pp. 606–624

    work page 2018

  14. [14]

    Improved techniques for training GANs,

    T. Salimans, I. Goodfellow, W. Zaremba,et al., “Improved techniques for training GANs,” inAdv. Neural Inf. Process. Syst., vol. 29, 2016

    work page 2016

  15. [15]

    Unit-ddpm: Unpaired image translation with denoising diffusion probabilistic models,

    H. Sasaki, C. G. Willcocks, and T. P. Breckon, “UNIT-DDPM: Unpaired image translation with denoising diffusion probabilistic models,” arXiv:2104.05358, 2021

    work page arXiv 2021

  16. [16]

    Attention based multi-layer fusion of multispectral images for pedestrian detection,

    Y . Zhang, Z. Yin, L. Nie, and S. Huang, “Attention based multi-layer fusion of multispectral images for pedestrian detection,”IEEE Access, vol. 8, pp. 165071–165084, 2019

    work page 2019

  17. [17]

    RTFNet: RGB-thermal fusion network for semantic segmentation of urban scenes,

    Y . Sun, W. Zuo, and M. Liu, “RTFNet: RGB-thermal fusion network for semantic segmentation of urban scenes,”IEEE Robot. Autom. Lett., vol. 4, no. 3, pp. 2576–2583, 2019. 8

    work page 2019

  18. [18]

    Image fusion in the loop of high-level vision tasks: A semantic-aware real-time infrared and visible image fusion network,

    L. Tang, J. Yuan, and J. Ma, “Image fusion in the loop of high-level vision tasks: A semantic-aware real-time infrared and visible image fusion network,”Information Fusion, vol. 82, pp. 28–42, 2022

    work page 2022

  19. [19]

    Global trends in satellite-based emergency mapping,

    S. V oigt, F. Giulio-Tonolo, J. Lyons,et al., “Global trends in satellite-based emergency mapping,”Science, vol. 353, no. 6296, pp. 247–252, 2016

    work page 2016

  20. [20]

    V ollmer and K.-P

    M. V ollmer and K.-P. Möllmann,Infrared Thermal Imaging: Fundamentals, Research and Applications, 2nd ed. Weinheim, Germany: Wiley-VCH, 2018

    work page 2018

  21. [21]

    Combating the heat island effect with drone-based thermal visualization,

    K. Gibbons, “Combating the heat island effect with drone-based thermal visualization,”Journal of Urban Affairs, pp. 1–10, 2025, doi: 10.1080/07352166.2025.2526493. 9

    work page doi:10.1080/07352166.2025.2526493 2025