Low-Light Image Enhancement Using Hybrid Deep-Learning and Mixed-Norm Loss Functions
Abstract
:1. Introduction
2. Proposed Method
2.1. Hybrid Deep-Learning Structure
2.2. Mixed Norm-Based Loss Function
3. Experimental Results
3.1. Experimental Setup
- (1)
- gamma correction: ,
- (2)
- random spray Gaussian noise: random spray ratio (0.01%) and Gaussian std. .
3.2. Analyses of Experimental Results
4. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Evaluator | Ground Truth | Degraded Image | MSR- Net [18] | Retinex- Net [20] | MBLLEN [21] | KinD [22] | Proposed Method | |
---|---|---|---|---|---|---|---|---|
with reference | N/A | 8.69 | 15.88 | 17.64 | 19.60 | 20.14 | 22.01 | |
N/A | 0.547 | 0.800 | 0.766 | 0.823 | 0.873 | 0.897 | ||
N/A | 282.90 | 210.94 | 374.09 | 202.57 | 327.84 | 208.04 | ||
N/A | 0.366 | 0.508 | 0.451 | 0.556 | 0.613 | 0.656 | ||
36.94 | 39.83 | 37.60 | 47.47 | 47.41 | 51.17 | 30.96 | ||
CPP | 35.98 | 15.07 | 29.36 | 47.50 | 25.82 | 30.03 | 31.27 | |
without reference | N/A | 33.25 | 31.06 | 39.25 | 52.65 | 46.17 | 24.02 | |
CPP | N/A | 13.93 | 19.64 | 35.66 | 14.44 | 20.01 | 20.63 |
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Oh, J.; Hong, M.-C. Low-Light Image Enhancement Using Hybrid Deep-Learning and Mixed-Norm Loss Functions. Sensors 2022, 22, 6904. https://doi.org/10.3390/s22186904
Oh J, Hong M-C. Low-Light Image Enhancement Using Hybrid Deep-Learning and Mixed-Norm Loss Functions. Sensors. 2022; 22(18):6904. https://doi.org/10.3390/s22186904
Chicago/Turabian StyleOh, JongGeun, and Min-Cheol Hong. 2022. "Low-Light Image Enhancement Using Hybrid Deep-Learning and Mixed-Norm Loss Functions" Sensors 22, no. 18: 6904. https://doi.org/10.3390/s22186904