From Early Models to Modern Techniques: A Deep Learning Survey on Single Image Super-Resolution
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".