激光与光电子学进展, 2020, 57 (14): 141021, 网络出版: 2020-07-28 复制并引用该论文  本文已被引 1 次  被引通知

图 & 表

图 1. 卷积块注意力模块结构

Fig. 1. Structure of convolutional block attention module

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图 2. 多分支全卷积神经网络架构

Fig. 2. Architecture of multi-branch all convolutional neural network

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图 3. 噪声提取模块

Fig. 3. Noise extraction module

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图 4. 不含噪声的合成低照度图像的主观视觉对比。(a) Parrots图像;(b) building2图像;(c) buildings图像;(d) monarch图像

Fig. 4. Subjective visual comparison of synthetic low-light images without noise. (a) Image of parrots; (b) image of building2; (c) image of buildings; (d) image of monarch

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图 5. 含噪声的合成低照度图像的主观视觉对比。(a) Parrots图像;(b) building2图像;(c) buildings图像;(d) monarch图像

Fig. 5. Subjective visual comparison of synthetic low-light images with noise. (a) Image of parrots; (b) image of building2; (c) image of buildings; (d) image of monarch

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图 6. 真实低照度图像的主观视觉对比。(a)(b)来自LIME数据库的图像;(c)(d)来自DICM数据库的图像;(e)来自MEF数据库的图像

Fig. 6. Subjective visual comparison of real low-light images. (a)(b) Images from LIME dataset; (c)(d) images from DICM dataset; (e) image from MEF dataset

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表 1不含噪声的合成低照度图像的客观评价指标对比

Table1. Comparison of objective evaluation indicators for synthetic low-light images without noise

Image PSNR/dB MSE MAE MS-SSIM Q VIF Originalimage 10.036/9.460 6447.9000/7440.8000 74.8505/76.8100 0.3925/0.3400 0.2407/0.2400 0.4889/0.5100 Image ofCLAHE[3] 15.596/15.850 1792.4000/1817.0000 39.3139/36.4600 0.7988/0.7815 0.6357/0.6900 0.7582/0.7500 Image ofSSR[6] 22.389/20.560 375.1150/658.8000 14.3500/19.1700 0.8247/0.7800 0.6489/0.6800 0.8437/0.7400 Image ofMSRCR[8] 11.135/11.410 5006.9000/4775.5000 67.8155/62.9100 0.7602/0.6900 0.6030/0.5900 0.6348/0.5800 Image of methodin Ref. [12] 19.040/17.430 811.0260/1220.0000 22.8847/28.7300 0.8001/0.7400 0.6243/0.6300 0.6229/0.4200 Image of methodin Ref. [30] 19.959/18.250 656.4077/1028.0000 18.3382/22.6600 0.8439/0.7800 0.6522/0.6500 0.6801/0.6700 Image of methodin Ref. [16] 22.679/21.860 350.8300/434.6700 16.5200/17.3000 0.9130/0.8650 0.7360/0.7640 0.8750/0.8510 Image ofMBACNN 23.869/22.550 266.7760/384.2000 13.8820/15.5000 0.9229/0.8700 0.7735/0.7700 0.8834/0.8630

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表 2含噪声的合成低照度图像上的客观评价指标对比

Table2. Comparison of objective evaluation indicators for synthetic low-light images with noise

Image PSNR/dB MSE MAE MS-SSIM Q VIF Originalimage 9.8380/8.7000 6749.6000/8878.0000 74.8583/84.9000 0.3050/0.2200 0.1364/0.0800 0.3249/0.3400 Image ofCLAHE[3] 13.4239/11.9600 2955.9000/4215.0000 49.0863/56.7000 0.5606/0.4100 0.3990/0.2700 0.5170/0.5100 Image ofSSR[6] 18.9663/18.3000 825.0010/979.0000 21.3554/23.6000 0.6690/0.5100 0.5018/0.4000 0.6058/0.5800 Image ofMSRCR[8] 10.4440/17.0400 5870.6000/1344.0000 72.3333/28.9000 0.5592/0.5000 0.3809/0.3750 0.5436/0.6280 Image of methodin Ref. [12] 19.5938/16.2000 714.0061/1600.0000 19.2488/33.7000 0.6630/0.4800 0.4639/0.4100 0.5216/0.4200 Image of methodin Ref. [30] 17.8858/16.5000 1058.0000/1535.7000 22.6885/30.3000 0.6145/0.5400 0.3896/0.3200 0.4593/0.4700 Image of methodin Ref. [16] 18.3000/19.3300 961.3000/776.5500 25.1700/21.6700 0.6900/0.6200 0.5300/0.4800 0.7370/0.7190 Image ofMBACNN 21.1500/19.8200 499.0299/697.3000 16.3177/20.2400 0.7970/0.6800 0.6251/0.5200 0.6903/0.7400

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表 3真实低照度图像的客观评价指标对比

Table3. Comparison of objective evaluation indicators for real low-light images

Image NRSS Entropy of information NIQE Image of CLAHE[3] 0.8862/0.9500 7.6180/7.3000 3.9221/5.2000 Image of SSR[6] 0.9257/0.9400 7.6845/7.3600 4.2682/5.0100 Image of MSRCR[8] 0.8946/0.9300 7.3285/7.5300 3.9577/4.2600 Image of method in Ref. [12] 0.9159/0.9190 7.7841/7.4600 4.1566/4.6100 Image of method in Ref. [30] 0.8785/0.9220 7.2858/7.4180 4.4679/4.9800 Image of method in Ref. [16] 0.9240/0.9630 6.6100/6.2250 5.1130/5.2530 Image of MBACNN 0.9359/0.9790 7.5844/7.4900 5.3669/4.6770

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表 4算法处理时间对比

Table4. Algorithm processing time comparison

Method Training time /h Test time /s CLAHE[3] — 0.77 SSR[6] — 2.65 MSRCR[8] — 1.02 Method in Ref. [12] — 0.72 Method in Ref. [30] — 1.66 Method in Ref. [16] 8.96 5.88 MBACNN 5.99 6.78

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吴若有, 王德兴, 袁红春, 宫鹏, 陈冠奇, 王丹. 基于多分支全卷积神经网络的低照度图像增强[J]. 激光与光电子学进展, 2020, 57(14): 141021. Ruoyou Wu, Dexing Wang, Hongchun Yuan, Peng Gong, Guanqi Chen, Dan Wang. Low-Light Image Enhancement Based on Multi-Branch All Convolutional Neural Network[J]. Laser & Optoelectronics Progress, 2020, 57(14): 141021.