JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE) ›› 2020, Vol. 55 ›› Issue (9): 72-80.doi: 10.6040/j.issn.1671-9352.0.2020.202

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Low-light image enhancement algorithm based on variational structure

Ming-xing LIN2,*()   

  1. 1. Techanical and Electrical Engineering Department, Shandong Transport Vocational College, Weifang 261206, Shandong, China
    2. School of Mechanical Engineering, Shandong University, Jinan 250061, Shandong, China
  • Received:2020-05-08 Online:2020-09-20 Published:2020-09-17
  • Contact: Ming-xing LIN E-mail:mxLin@sdu.edu.cn

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Abstract:

Aiming at the problem that the images captured under low illumination present some defects, such as low brightness and poor visibility, a low-light image enhancement algorithm based on variational structure guided filtering is proposed. First, the maximum of R, G and B channels are searched at each pixel of the input image, and the bright channel map is obtained by using the maximum filtering. Secondly, a novel guided filtering based on variational model is constructed to refine the bright channel image to estimate the illumination component. Subsequently, the reflection component is obtained by removing the illumination component according to the Retinex model. Finally, homomorphic filtering and linear stretching are employed to further improve the contrast of the reflection component to output an enhanced image with clear visibility. Comprehensive experimental results show that this algorithm can effectively and quickly enhance the illumination and contrast of low-light images and retain details.

Key words: low-light image, Retinex, variational model, guided filtering, homomorphic filtering

CLC Number: 

  • TP391.41

Fig.1

Retinex model"

Fig.2

Flow chart of the proposed algorithm"

Fig.3

Halo effect"

Fig.4

Estimated illumination"

Fig.5

Comparison of low-light images processed by four enhancement algorithms"

Fig.6

Enlarged local patches enhanced by four enhancement algorithms"

Table 1

Comparison of different algorithms in terms of evaluation index"

测试图像 评价指标 原图 MSRCR算法 ALSM算法 NPEA算法 本文算法
图a 对比度 509.95 513.34 466.77 616.71 531.88
信息熵 3.58 4.45 5.06 4.93 5.22
LOE — 266.40 396.55 214.57 188.79
运行时间(s) — 1.01 28.85 28.59 1.53
图b 对比度 210.25 217.46 696.55 1 057.02 440.52
信息熵 4.02 3.89 4.66 5.17 4.81
LOE — 194.76 294.01 679.54 232.50
运行时间(s) — 1.28 55.74 58.87 2.76
图c 对比度 8.79 337.74 79.97 384.38 446.81
信息熵 1.03 2.79 1.14 1.91 3.26
LOE — 354.97 256.44 301.35 305.08
运行时间(s) — 1.30 32.59 46.47 1.83
图d 对比度 45.75 1 530.11 1 300.21 1 242.26 2 267.35
信息熵 3.02 5.53 5.39 5.76 6.27
LOE — 184.42 207.60 467.38 129.01
运行时间(s) — 1.07 16.97 21.14 1.09
图e 对比度 121.87 122.05 227.60 342.48 346.26
信息熵 2.88 3.08 4.21 3.78 4.09
LOE — 333.30 256.60 283.84 287.54
运行时间(s) — 4.52 178.43 133.72 11.01
图f 对比度 166.80 327.72 439.99 334.66 441.50
信息熵 3.92 3.49 3.55 3.35 3.78
LOE — 1 796.78 389.27 703.72 291.33
运行时间(s) — 1.95 91.84 55.95 4.33
图g 对比度 385.02 427.07 2 120.26 700.07 882.83
信息熵 4.35 4.62 5.62 5.22 5.37
LOE — 1 622.83 238.81 271.75 257.89
运行时间(s) — 1.23 40.07 31.89 1.87
图h 对比度 361.12 346.66 523.40 471.64 553.95
信息熵 4.42 4.67 5.25 5.00 5.18
LOE — 168.73 364.20 345.64 192.04
运行时间(s) — 1.16 16.15 24.08 1.19
平均值 对比度 226.19 477.77 731.84 643.65 737.89
信息熵 3.40 4.07 4.36 4.39 4.75
LOE — 615.27 300.43 408.47 235.52
运行时间(s) — 1.69 57.58 50.09 3.20
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