Study of Deep Learning-based models for Single Image Super-Resolution
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 super-resolution of images has seen remarkable progress, especially with the use of deep learning models.This technique allows having a better-quality image from one or more low-resolution versions.Super-resolution, therefore, aims at enriching a lowresolution image with additional pixel density and high-frequency detail.This paper presents a comprehensive empirical study based on a systematic review of deep learningbased models for single image super-resolution (SISR), exploring the set of techniques offered by deep learning technology and used for SISR.In this paper, we present a global and complete state of the art on deep learning model based on reference metrics (mainly Peak Signal to Noise Ratio -PSNR-and Structural SIMilarity -SSIM-) in the field of computer visualization and image reconstruction.This study was done on several deep learning designs with 90 different models tested on 7 reference datasets in the computer vision domain.Thus, our goal is to present a benchmark to demonstrate the performance and limitations of these models as well as to guide future research in the field of super image resolution to develop efficient algorithms.Moreover, our study covers different neural network architectures (Generative Adversarial Networks -GAN-
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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 | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 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