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摘要: 为将低照度图像及基于它生成的多个不同曝光度图像中的互补性信息进行最佳融合以获得更为鲁棒的视觉增强效果, 提出了一种基于多图像局部结构化融合的两阶段低照度图像增强(Low-light image enhancement, LLIE)算法. 在待融合图像制备阶段, 提出了一种基于图像质量评价的最佳曝光度预测模型, 利用该预测模型给出的关于低照度图像最佳曝光度值, 在伪曝光模型下生成适度增强图像和过曝光图像 (利用比最佳曝光度值更高的曝光度生成)各一幅. 同时, 利用经典Retinex模型生成一幅适度增强图像作为补充图像参与融合. 在融合阶段, 首先将低照度图像、适度增强图像(2幅)和过曝光图像在同一空间位置处的图块矢量化后分解为对比度、结构强度和亮度三个分量. 之后, 以所有待融合对比度分量中的最高值作为融合后的对比度分量值, 而结构强度和亮度分量则分别以相位一致性映射图和视觉显著度映射图作为加权系数完成加权融合. 然后, 将分别融合后的对比度、纹理结构和亮度三个分量重构为图块, 并重新置回融合后图像中的相应位置. 最后, 在噪声水平评估算法导引下自适应调用降噪算法完成后处理. 实验结果表明: 所提出的低照度图像增强算法在主客观图像质量评价上优于现有大多数主流算法.Abstract: To combine the complementary information of the multi-exposure images generated from a given low-light image, a two-stage low-light image enhancement (LLIE) algorithm adopting multi-image local structural fusion approach was proposed to produce a fused image that is more informative and robust than each one. Specifically, in the image preparation stage, an optimal exposure prediction model based on image quality assessment was first built. Then a well-exposed image and an over-exposed image were generated with corresponding exposure ratios estimated with the prediction model for a given low-light image, respectively. Simultaneously, the classical Retinex model was used to obtain another well-exposed image to provide more supplementary information to be fused. In the fusion stage, the patches extracted from the low-light image, two well-exposed images, and the over-exposed image at the same spatial positions were vectorized and decomposed into independent components, i.e., contrast, texture structure, and brightness. The desired contrast of the fused image patch was determined by the highest contrast of all source image patches, while the structural strength and brightness components were weighted with phase congruency map and visual saliency map, respectively. Upon fusing these three components separately, we reconstructed a desired patch and placed it back into the fused image. Finally, a denoising algorithm whose input parameter is estimated by a noise level estimation algorithm was exploited to suppress the accompanying noise due to enhancement process. The experimental results show that, the proposed LLIE algorithm outperforms the existing the state-of-art ones in the terms of both subjective and objective image quality assessment.
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图 1 Ying算法中实现低照度图像增强的融合框架
Fig. 1 Fusion framework of low-light image enhancement in Ying algorithm
图 2 待融合图像纹理结构权重的比较分析
Fig. 2 The weight analysis of the texture structure component for images to be fused
图 3 待融合图像亮度值权重的比较分析
Fig. 3 The weight analysis of the brightness component for images to be fused
图 5 各个算法在Tree图上增强效果对比
Fig. 5 Visual comparison of the results obtained with competing algorithms on Tree image
图 7 各个算法在Class图上增强效果对比
Fig. 7 Visual comparison of the results obtained with competing algorithms on Class image
图 6 各个算法在Tower图上增强效果对比
Fig. 6 Visual comparison of the results obtained with competing algorithms on Tower image
图 8 各个算法在Community图像上抑制噪声效果对比
Fig. 8 Comparison of denoising effect of each algorithm on Community image
表 1 各个对比算法对Tree图像增强后的无参考指标值比较
Table 1 Performance comparison of each algorithm on Tree image
算法 HVS Fu2016a Fu2016b Ying LIME Li FFMD MLSF-LLIE BOIEM 1.96 2.07 2.18 1.97 2.11 2.26 2.25 2.37 BIQME 0.28 0.30 0.37 0.35 0.48 0.37 0.43 0.39 NIQMC 3.31 3.66 4.18 3.27 4.69 3.54 3.40 4.17 IL-NIQE 57.66 60.04 57.39 58.69 58.08 49.92 65.06 31.79 
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表 2 各个对比算法对图像Tower增强后的无参考指标值
Table 2 Performance comparison of each algorithm on Tower image
算法 HVS Fu2016a Fu2016b Ying LIME Li FFMD MLSF-LLIE BOIEM 2.30 3.42 3.04 2.90 3.18 2.77 3.42 3.15 BIQME 0.40 0.54 0.48 0.53 0.54 0.53 0.58 0.56 NIQMC 3.48 4.97 4.58 4.63 4.63 4.22 4.92 4.70 IL-NIQE 42.82 32.34 37.05 34.68 35.04 38.66 36.75 30.10
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表 3 各个对比算法对图像Class增强后的无参考指标值
Table 3 Performance comparison of each algorithm on Class image
算法 HVS Fu2016a Fu2016b Ying LIME Li FFMD MLSF-LLIE BOIEM 2.67 2.68 3.42 2.64 3.38 2.61 2.63 3.17 BIQME 0.48 0.50 0.57 0.53 0.6 0.55 0.53 0.56 NIQMC 4.57 4.57 5.28 4.67 5.00 4.67 4.33 4.95 IL-NIQE 25.89 22.22 20.9 24.26 22.99 38.22 24.47 20.97 IW-PNSR 16.68 17.36 21.36 18.66 15.24 16.46 17.93 22.18 IW-SSIM 0.85 0.87 0.94 0.91 0.87 0.83 0.88 0.95
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表 4 各个算法在90幅无参考低照度图像上的无参考指标的平均值
Table 4 Average performance of different competing algorithms on 90 low-light images without reference images
算法 HVS Fu2016a Fu2016b Ying LIME Li FFMD MLSF-LLIE BOIEM 3.07 3.17 3.27 3.13 3.38 3.12 3.03 3.21 BIQME 0.54 0.56 0.57 0.57 0.61 0.58 0.58 0.58 NIQMC 4.73 4.88 5.02 4.81 5.33 4.8 4.64 4.89 IL-NIQE 28.69 27.18 27.15 27.61 28.21 30.94 28.62 25.28
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表 5 各个算法在90幅有参考低照度图像上的无参考和有参考指标的平均值
Table 5 Average performance of different competing algorithms on 90 low-light images with reference images
算法 HVS Fu2016a Fu2016b Ying LIME Li FFMD MLSF-LLIE BOIEM 2.67 2.68 3.42 2.64 3.38 2.61 2.63 3.17 BOIEM 3.38 3.41 3.43 3.34 3.43 3.38 3.28 3.40 BIQME 0.58 0.60 0.58 0.58 0.61 0.58 0.59 0.59 NIQMC 5.11 5.15 5.10 4.97 5.34 5.06 4.84 5.02 IL-NIQE 23.14 22.74 22.42 22.29 24.69 25.54 22.62 22.19 IW-PSNR 20.35 19.81 22.10 22.15 15.31 22.04 21.88 22.54 IW-SSIM 0.92 0.91 0.94 0.94 0.84 0.93 0.94 0.95
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表 6 各个算法在90幅有参考低照度图像上的平均执行时间比较(s)
Table 6 The average execution time of the competing algorithms on 90 low-light images with reference images (s)
算法 HVS Fu2016a Fu2016b Ying LIME Li FFMD MLSF-LLIE 时间 0.56 0.29 1.93 0.21 0.10 11.23 4.13 4.21
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