From Early Models to Modern Techniques: A Deep Learning Survey on 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 primary goal of Single Image Super-Resolution (SISR), a fundamental yet challenging computer vision task with several practical applications in domains such as surveillance, medical imaging, and remote sensing, is to reconstruct a high-resolution (HR) image from a single low- resolution (LR) input. The performance of SISR has been greatly improved by the advent of deep learning, specifically Convolutional Neural Networks (CNNs) and Transformer architectures. An extensive review of deep learning-based SISR techniques is presented in this study. Begin by formulating the SISR problem and discussing prevalent evaluation metrics that balance distortion (e.g., PSNR) and perceptual quality (e.g., SSIM, LPIPS). Subsequently, classifying and analyzing key methodologies across five categories: interpolation-based and traditional models, CNN-based architectures (e.g., SRCNN, VDSR, EDSR), GAN-based frameworks (e.g., SRGAN, ESRGAN, Real-ESRGAN), attention-enhanced networks (e.g., RCAN), and Transformer-based approaches (e.g., SwinIR, HAT). In each category, the theoretical framework, design innovations, and corresponding advantages and limitations are explored. By showing architectural design strategies and training paradigms, this review highlights a structured understanding of the significant evolution from early CNNs to sophisticated GANs and Transformers in SISR, serving as a reference for future model development and practical deployment.
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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 | 0.001 |
| 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