A Comparative Evaluation of Deep Learning Paradigms for Low-Light Image Enhancement: From CNNs to Diffusion Models
Bibliographic record
Abstract
Low-light image enhancement (LLIE) has attracted extensive research interest, with approaches spanning convolutional neural networks (CNNs), Retinex-based deep architectures, zero-shot learning, generative adversarial networks (GANs), Transformers, and diffusion models. Despite the proliferation of individual methods, a unified cross-paradigm evaluation under consistent experimental conditions remains absent in the existing literature. This paper presents a systematic comparative study of twelve representative LLIE methods drawn from six distinct algorithmic paradigms. All methods are evaluated on three widely adopted benchmark datasets—LOL-v1, LOL-v2-Real, and SID—using both full-reference metrics (PSNR, SSIM, LPIPS) and no-reference metrics (NIQE, BRISQUE), alongside computational efficiency analysis covering parameter count, floating-point operations, and inference latency. The experimental results indicate that Transformer-based approaches achieve a favorable balance between reconstruction fidelity and perceptual quality, while zero-shot methods offer substantial advantages in inference speed at the cost of quantitative performance. Diffusion-based methods produce perceptually compelling outputs but incur considerable computational overhead. Cross-dataset generalization tests further expose performance degradation across all paradigms when trained and tested on mismatched data distributions. These findings provide practical guidance for selecting LLIE methods under different deployment constraints and evaluation priorities.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".