A Conditional GAN Architecture for Colorization of Thermal Infrared Images
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The applicability of visible spectrum cameras is limited to nighttime and extreme weather conditions. To overcome these limitations, infrared (IR) cameras were introduced, but their images lack luminance and representation quality, limiting the analytical ability and response time of humans. To be understandable by humans, image enhancement is not sufficient; conversion to visible RGB format is required, and this process is popularly known as colorization. However, the thermal infrared (TIR) images are low in both luminance and chrominance in comparison to grayscale images, which are only low in chrominance. Therefore, TIR colorization needs image-to-image translation; simple color transfer is not enough. In this paper, we investigated and modified one of the most commonly used conditional generative adversarial networks, known as pix2pixHD GAN for TIR-to-visible RGB translation. We are proposing a new composite loss function with noise augmentation in training. The improvement in the average values of NRMSE, PSNR, LPIPS, and NIQE is observed when compared with the state-of-the-art on the publicly available KAIST dataset. The results of the extensive experiments proved the effectiveness of the proposed method for TIR colorization, which is shown using both subjective (visual) and objective assessments for evaluation of image quality.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it