Deep Learning Thermal Image Translation for Night Vision Perception
Why this work is in the frame
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Bibliographic record
Abstract
Context enhancement is critical for the environmental perception in night vision applications, especially for the dark night situation without sufficient illumination. In this article, we propose a thermal image translation method, which can translate thermal/infrared (IR) images into color visible (VI) images, called IR2VI. The IR2VI consists of two cascaded steps: translation from nighttime thermal IR images to gray-scale visible images (GVI), which is called IR-GVI; and the translation from GVI to color visible images (CVI), which is known as GVI-CVI in this article. For the first step, we develop the Texture-Net, a novel unsupervised image translation neural network based on generative adversarial networks. Texture-Net can learn the intrinsic characteristics from the GVI and integrate them into the IR image. In comparison with the state-of-the-art unsupervised image translation methods, the proposed Texture-Net is able to address some common challenges, e.g., incorrect mapping and lack of fine details, with a structure connection module and a region-of-interest focal loss. For the second step, we investigated the state-of-the-art gray-scale image colorization methods and integrate the deep convolutional neural network into the IR2VI framework. The results of the comprehensive evaluation experiments demonstrate the effectiveness of the proposed IR2VI image translation method. This solution will contribute to the environmental perception and understanding in varied night vision applications.
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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