Low-Illuminance Image Processing Based on Brightness Channel Detail Enhancement

Yichun Jiang; Weida Zhan; Depeng Zhu

doi:10.3788/LOP202158.0410001

Journals >Laser & Optoelectronics Progress >Volume 58 >Issue 4 >Page 0410001 > Article

Laser & Optoelectronics Progress
Vol. 58, Issue 4, 0410001 (2021)

Low-Illuminance Image Processing Based on Brightness Channel Detail Enhancement

Yichun Jiang, Weida Zhan^*, and Depeng Zhu

Author Affiliations

School of Electronic Information Engineering, Changchun University of Science and Technology, Changchun, Jilin 130022, China

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DOI: 10.3788/LOP202158.0410001 Cite this Article Set citation alerts

Yichun Jiang, Weida Zhan, Depeng Zhu. Low-Illuminance Image Processing Based on Brightness Channel Detail Enhancement[J]. Laser & Optoelectronics Progress, 2021, 58(4): 0410001 Copy Citation Text

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Fig. 1. Flow chart of proposed algorithm

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Fig. 2. Contrast before and after details enhancement. (a) Before processing; (b)after processing

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Fig. 3. Comparison of the enhancement effects of various algorithms in scene1.(a) Original image; (b) MSR; (c) ALTM; (d) MF; (e) Ref. [14] algorithm; (f) proposed algorithm

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Fig. 4. Comparison of the enhancement effects of various algorithms in scene2. (a) Original image; (b)MSR; (c) ALTM; (d) MF; (e) Ref. [14] algorithm; (f) proposed algorithm

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Fig. 5. Comparison of the enhancement effects of various algorithms in scene3. (a) Original image; (b) MSR; (c) ALTM; (d) MF; (e) Ref. [14] algorithm; (f) proposed algorithm

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Fig. 6. Comparison of the enhancement effects of various algorithms in scene4. (a) Original image; (b) MSR; (c) ALTM; (d) MF; (e) Ref. [14] algorithm; (f) proposed algorithm

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Fig. 7. Comparison of the enhancement effects of various algorithms in scene5. (a) Original image; (b) MSR; (c) ALTM; (d) MF; (e) Ref. [14] algorithm; (f) proposed algorithm

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Algorithm	Parameter
MSR	σ₁=15,σ₂=80,σ₃=250
ALTM	σ=0.001
MF	σ=0.025,μ=0.5,α=2,φ=250°
Ref. [14]	ε=0.01, λ_r=0.001,λ_s=0.01,λ_b=0.15

Table 1. Selection of parameters for comparison algorithms

Figure	MSR	ALTM	MF	Ref. [14] algorithm	Proposed algorithm
Fig. 3	328.6	215.1	433.7	636.7	231.5
Fig. 4	352.8	833.9	760.7	687.8	145.4
Fig. 5	255.8	488.0	609.5	566.6	176.1
Fig. 6	420.3	652.2	454.0	701.7	283.9
Fig. 7	381.1	202.3	675.0	902.9	324.1
Average	347.7	478.3	586.6	699.1	232.2

Table 2. Quantitative evaluation table of LOE

Figure	Original image	MSR	ALTM	MF	Ref. [14] algorithm	Proposed algorithm
Fig. 3	6.3698	6.5552	7.3236	7.5411	7.3504	7.3883
Fig. 4	5.7130	6.1958	6.2335	5.9350	6.2706	6.6912
Fig. 5	5.9424	6.8171	6.8503	6.5728	6.6456	7.1247
Fig. 6	5.8426	6.8431	6.8884	6.8987	6.8589	6.9993
Fig. 7	6.7449	7.4020	7.4344	7.5643	7.3847	7.6231

Table 3. Quantitative evaluation table of entropy

Figure	Original image	MSR	ALTM	MF	Ref. [14] algorithm	Proposed algorithm
Fig. 3	0.0123	0.0166	0.0153	0.0196	0.0162	0.0165
Fig. 4	0.0148	0.0380	0.0351	0.0275	0.0248	0.0415
Fig. 5	0.0168	0.0506	0.0401	0.0364	0.0313	0.0453
Fig. 6	0.0226	0.0369	0.0358	0.0389	0.0338	0.0448
Fig. 7	0.0637	0.0604	0.0709	0.0934	0.0828	0.0841

Table 4. Quantitative evaluation table of gradient energy

Yichun Jiang, Weida Zhan, Depeng Zhu. Low-Illuminance Image Processing Based on Brightness Channel Detail Enhancement[J]. Laser & Optoelectronics Progress, 2021, 58(4): 0410001

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