TPCNet: Triple physical constraints for Low-light Image Enhancement Article Swipe
Low-light image enhancement is an essential computer vision task to improve image contrast and to decrease the effects of color bias and noise. Many existing interpretable deep-learning algorithms exploit the Retinex theory as the basis of model design. However, previous Retinex-based algorithms, that consider reflected objects as ideal Lambertian ignore specular reflection in the modeling process and construct the physical constraints in image space, limiting generalization of the model. To address this issue, we preserve the specular reflection coefficient and reformulate the original physical constraints in the imaging process based on the Kubelka-Munk theory, thereby constructing constraint relationship between illumination, reflection, and detection, the so-called triple physical constraints (TPCs)theory. Based on this theory, the physical constraints are constructed in the feature space of the model to obtain the TPC network (TPCNet). Comprehensive quantitative and qualitative benchmark and ablation experiments confirm that these constraints effectively improve the performance metrics and visual quality without introducing new parameters, and demonstrate that our TPCNet outperforms other state-of-the-art methods on 10 datasets.
Related Topics
Concepts
Artificial intelligence
Constraint (computer-aided design)
Computer vision
Computer science
Specular reflection
Benchmark (surveying)
Color constancy
Process (computing)
Generalization
Feature (linguistics)
Image (mathematics)
Reflection (computer programming)
Image quality
Basis (linear algebra)
Feature extraction
Perspective (graphical)
Feature vector
Construct (python library)
Mathematics
Image restoration
Exploit
Pattern recognition (psychology)
Algorithm
Task (project management)
Iterative reconstruction
Contrast (vision)
Image enhancement
Image compression
Image processing
Visualization
Artificial neural network
Metadata
- Type
- article
- Landing Page
- http://arxiv.org/abs/2511.22052
- https://arxiv.org/pdf/2511.22052
- OA Status
- green
- OpenAlex ID
- https://openalex.org/W7108247820
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W7108247820Canonical identifier for this work in OpenAlex
- Title
-
TPCNet: Triple physical constraints for Low-light Image EnhancementWork title
- Type
-
articleOpenAlex work type
- Publication year
-
2025Year of publication
- Publication date
-
2025-11-27Full publication date if available
- Authors
-
Shi Jing-yi, Li-ming Fei, Wu Ling AnList of authors in order
- Landing page
-
https://arxiv.org/abs/2511.22052Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2511.22052Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2511.22052Direct OA link when available
- Concepts
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Artificial intelligence, Constraint (computer-aided design), Computer vision, Computer science, Specular reflection, Benchmark (surveying), Color constancy, Process (computing), Generalization, Feature (linguistics), Image (mathematics), Reflection (computer programming), Image quality, Basis (linear algebra), Feature extraction, Perspective (graphical), Feature vector, Construct (python library), Mathematics, Image restoration, Exploit, Pattern recognition (psychology), Algorithm, Task (project management), Iterative reconstruction, Contrast (vision), Image enhancement, Image compression, Image processing, Visualization, Artificial neural networkTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
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| abstract_inverted_index.physical | 59, 83, 106, 114 |
| abstract_inverted_index.preserve | 74 |
| abstract_inverted_index.previous | 39 |
| abstract_inverted_index.specular | 50, 76 |
| abstract_inverted_index.(TPCNet). | 130 |
| abstract_inverted_index.Low-light | 0 |
| abstract_inverted_index.benchmark | 135 |
| abstract_inverted_index.construct | 57 |
| abstract_inverted_index.datasets. | 166 |
| abstract_inverted_index.essential | 5 |
| abstract_inverted_index.reflected | 44 |
| abstract_inverted_index.so-called | 104 |
| abstract_inverted_index.Lambertian | 48 |
| abstract_inverted_index.algorithms | 27 |
| abstract_inverted_index.constraint | 96 |
| abstract_inverted_index.detection, | 102 |
| abstract_inverted_index.reflection | 51, 77 |
| abstract_inverted_index.algorithms, | 41 |
| abstract_inverted_index.coefficient | 78 |
| abstract_inverted_index.constraints | 60, 84, 107, 115, 142 |
| abstract_inverted_index.constructed | 117 |
| abstract_inverted_index.demonstrate | 156 |
| abstract_inverted_index.effectively | 143 |
| abstract_inverted_index.enhancement | 2 |
| abstract_inverted_index.experiments | 138 |
| abstract_inverted_index.introducing | 152 |
| abstract_inverted_index.outperforms | 160 |
| abstract_inverted_index.parameters, | 154 |
| abstract_inverted_index.performance | 146 |
| abstract_inverted_index.qualitative | 134 |
| abstract_inverted_index.reflection, | 100 |
| abstract_inverted_index.reformulate | 80 |
| abstract_inverted_index.Kubelka-Munk | 92 |
| abstract_inverted_index.constructing | 95 |
| abstract_inverted_index.quantitative | 132 |
| abstract_inverted_index.relationship | 97 |
| abstract_inverted_index.(TPCs)theory. | 108 |
| abstract_inverted_index.Comprehensive | 131 |
| abstract_inverted_index.Retinex-based | 40 |
| abstract_inverted_index.deep-learning | 26 |
| abstract_inverted_index.illumination, | 99 |
| abstract_inverted_index.interpretable | 25 |
| abstract_inverted_index.generalization | 65 |
| abstract_inverted_index.state-of-the-art | 162 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 3 |
| citation_normalized_percentile.value | 0.81776427 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |