Low-light True Color Image Enhancement Algorithm Based on Adaptive Truncation Simulation Exposure and Unsupervised Fusion

Yongcheng HAN; Wenwen ZHANG; Weiji HE; Qian CHEN

doi:10.3788/gzxb20235209.0910002

Journals >Acta Photonica Sinica >Volume 52 >Issue 9 >Page 0910002 > Article

Acta Photonica Sinica
Vol. 52, Issue 9, 0910002 (2023)

Low-light True Color Image Enhancement Algorithm Based on Adaptive Truncation Simulation Exposure and Unsupervised Fusion

Yongcheng HAN, Wenwen ZHANG^*, Weiji HE, and Qian CHEN

Author Affiliations

School of Electronic and Optical Engineering，University of Science and Technology，Nanjing 210094，China

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DOI: 10.3788/gzxb20235209.0910002 Cite this Article

Yongcheng HAN, Wenwen ZHANG, Weiji HE, Qian CHEN. Low-light True Color Image Enhancement Algorithm Based on Adaptive Truncation Simulation Exposure and Unsupervised Fusion[J]. Acta Photonica Sinica, 2023, 52(9): 0910002 Copy Citation Text

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Fig. 1. Adaptive gamma correction curve with truncation factor

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Fig. 2. Schematic diagram of enhancement sequence

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Fig. 3. Multi-exposure fusion process diagram based on deep learning

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Fig. 4. Context aggregation network structure diagram

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Fig. 5. Guided filtering layer

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Fig. 6. Comparison of enhancement effects in backlight environment

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Fig. 7. Comparison of enhancement effects in local light level environment

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Fig. 8. Comparison of enhancement effects in extremely low light environment

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Fig. 9. Sample images of laboratory environment testing images

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Fig. 10. Comparison of enhancement effects of different algorithms

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Fig. 11. Comparison of enhancement effects of different algorithms

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Fig. 12. Comparison of enhancement results of different illumination colorimetric cards

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Layer	1	2	3	4	5	6	7	8
Channel	24	24	24	24	24	24	24	1
Kernel	3×3	3×3	3×3	3×3	3×3	3×3	3×3	1×1
Dilation	1	2	5	1	2	5	1	1
Equivalent kernel	3×3	5×5	11×11	3×3	5×5	11×11	3×3	1×1
Receptive field	3×3	7×7	17×17	19×19	23×23	33×33	35×35	35×35

Table 1. Context aggregation network structure

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Input	Low resolution weight map $W_{k}^{l}$ Low resolution exposure sequence $X_{k}^{l}$ High resolution exposure sequence $X_{k}^{h}$
Output	High resolution weight map $W_{k}^{h}$
Step 1	$\{\begin{array}{l} \bar{X_{k}^{l}} = f_{m e a n} (X_{k}^{l}) \\ \bar{W_{k}^{l}} = f_{m e a n} (W_{k}^{l}) \\ \bar{X_{l}^{2}} = f_{m e a n} (X_{k}^{l} ⊙ X_{k}^{l}) \\ \bar{X_{l} W_{l}} = f_{m e a n} (X_{k}^{l} ⊙ W_{k}^{l}) \end{array}$	Step 3	$\{\begin{array}{l} A_{l} = \sum_{X_{l} W_{l}} / (\sum_{X_{l}} + ε) \\ B_{l} = \bar{W_{l}} - A_{l} ⊙ \bar{X_{l}} \end{array}$
Step 1		Step 4	$\{\begin{array}{l} A_{h} = f_{↑} (A_{l}) \\ B_{h} = f_{↑} (B_{l}) \end{array}$
Step 2	$\{\begin{array}{l} \sum_{X_{l}} = \bar{X_{l}^{2}} - \bar{X_{k}^{l}} ⊙ \bar{X_{k}^{l}} \\ \sum_{X_{l} W_{l}} = \bar{X_{l} W_{l}} - \bar{X_{k}^{l}} ⊙ \bar{W_{k}^{l}} \end{array}$	Step 5	$W_{k}^{h} = A_{k}^{h} ⊙ X_{k}^{h} + B_{k}^{h}$

Table 2. Algorithm steps of depth guided filter module

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	LIME	LECARM	FEM	KinD	RUAS	Zero-DCE	Proposed
DICM dataset	3.517 0	2.611 3	2.746 5	2.988 8	4.547 7	2.750 2	2.764 5
MEF dataset	4.732 9	3.509 7	3.413 2	3.375 3	3.483 1	3.380 8	3.130 1
NPE dataset	3.942 5	3.449 0	3.407 2	3.634 2	5.349 7	3.655 4	3.425 3
LIME dataset	4.411 1	4.325 8	4.404 6	4.453 0	4.284 0	4.138 8	3.952 5
Average	4.510 9	3.474 0	3.492 9	3.612 8	4.416 1	3.481 3	3.318 1

Table 3. NIQE comparison of enhancement results under different algorithms

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	LIME	LECARM	KinD	RUAS	Zero-DCE	Proposed
PSNR↑	14.766 0	15.506 5	14.990 8	15.568 1	13.742 6	16.170 6
SSIM↑	0.334 4	0.547 2	0.593 9	0.560 3	0.514 2	0.605 4
NIQE↓	6.738 2	7.132 1	4.185 5	6.831 9	7.081 9	6.481 7
Time↓	53 s	5 s	20 s	7 s	4 s	19 s

Table 4. Image quality evaluation and time spent comparison of enhancement results under different algorithms

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	LIME	LECARM	KinD	RUAS	Zero-DCE	Proposed
8.71×10^-2 lx	20.762 4	23.840 0	23.915 9	25.198 5	21.635 5	18.081 5
1.02×10^-2 lx	42.503 7	40.688 2	36.293 8	41.445 0	35.681 4	34.625 4

Table 5. Color difference comparison of color card image enhancement results under different illuminance

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