An Efficient Deep Unsupervised Superresolution Model for Remote Sensing Images
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Bibliographic record
Abstract
Superresolution (SR) has provided an effective solution to the increasing need for high-resolution images in remote sensing applications. Among various SR methods, deep learning-based SR (DLSR) has made a significant breakthrough. However, supervised DLSR methods require a considerable amount of training data, which is hardly available in the remote sensing field. To address this issue, some research works have recently proposed and revealed the capability of deep learning in unsupervised SR. This article presents an efficient unsupervised SR (EUSR) deep learning model using dense skip connections, which boosts the reconstruction performance in parallel with the reduction of computational burden. To do this, several blocks containing densely connected convolutional layers are implemented to increase the depth of the model. Some skip connections also concatenate feature maps of different blocks to enable better SR performance. Moreover, a bottle-neck block abstracts the feature maps in fewer feature maps to remarkably reduce the computational burden. According to our experiments, the proposed EUSR leads to better results than the state-of-the-art DLSR method in terms of reconstruction quality with less computational burden. Furthermore, results indicate that the EUSR is more robust than its rival in dealing with images of different classes and larger sizes.
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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 | 0.001 |
| 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