基于亮度通道细节增强的低照度图像处理 
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Low-Illuminance Image Processing Based on Brightness Channel Detail Enhancement
长春理工大学电子信息工程学院, 吉林 长春 130022
图 & 表
图 1. 本文算法流程图
Fig. 1. Flow chart of proposed algorithm
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图 2. 细节增强前后对比。(a)处理前;(b)处理后
Fig. 2. Contrast before and after details enhancement. (a) Before processing; (b)after processing
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图 3. 场景1的各算法增强效果对比。(a)原始图像;(b)MSR;(c)ALTM;(d)MF;(e)文献[
14]算法;(f)本文算法
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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图 4. 场景2的各算法增强效果对比。(a)原始图像;(b)MSR;(c)ALTM;(d)MF;(e)文献[
14]算法;(f)本文算法
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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图 5. 场景3的各算法增强效果对比。(a)原始图像;(b)MSR;(c)ALTM;(d)MF;(e)文献[
14]算法;(f)本文算法
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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图 6. 场景4的各算法增强效果对比。(a)原始图像;(b)MSR;(c)ALTM;(d)MF;(e)文献[
14]算法;(f)本文算法
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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图 7. 场景5的各算法增强效果对比。(a)原始图像;(b)MSR;(c)ALTM;(d)MF;(e)文献[
14]算法;(f)本文算法
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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表 1对比算法参数选取
Table1. Selection of parameters for comparison algorithms
| 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, λr=0.001,λs=0.01,λb=0.15 |
|
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表 2亮度失真定量评价表
Table2. Quantitative evaluation table of LOE
| 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 |
|
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表 3信息熵定量评价表
Table3. Quantitative evaluation table of entropy
| 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 |
|
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表 4能量梯度定量评价表
Table4. Quantitative evaluation table of gradient energy
| 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 |
|
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蒋一纯, 詹伟达, 朱德鹏. 基于亮度通道细节增强的低照度图像处理[J]. 激光与光电子学进展, 2021, 58(4): 0410001. 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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