Low-light Image Enhancement Based on Multi-scale Wavelet U-Net
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摘要: 成像系统实时采集的低光照环境图像具有照度低、噪声严重、视觉效果差等问题，为了提高低光照环境成像质量，本文提出基于多尺度小波U型网络的低光照图像增强方法。该方法采用多级编解码器构建U型网络，并引入小波变换构建特征分频单元，分离高频和低频信息，增强对低频照度特征和高频纹理信息的感知。设计多尺度感知损失函数，指导网络学习低频信息到高频信息的逐级重建，从而优化网络的收敛和性能。最后，在LOL、LIME、NPE、MEF、DICM和VV数据集上进行测试。实验结果表明，所提方法能够有效提升图像照度，抑制图像噪声和纹理丢失问题，并在PSNR、SSIM、LOE和NIQE评价指标上均优于比较算法，在主观和客观评价方面均优于其他对比算法。Abstract: The real-time images collected by imaging systems in low illumination environments suffer from low illumination, severe noise and poor visual effects. To improve the image quality in low light environments, this study proposes a low-light image enhancement method based on a multi-scale wavelet U-Net. This method uses multi-level encoders and decoders to construct a U-Net and introduces the wavelet transform to develop a unit for frequency decomposition of features. This improves the perception of illumination and texture by separating high-frequency and low-frequency information. Multi-scale perceived loss is designed to guide the learned mapping of step-by-step reconstruction from low-frequency information to high-frequency information, thereby optimizing the convergence and performance of the network. Finally, the proposed method and comparison methods are tested on the LOL, LIME, NPE, MEF, DICM and VV datasets. The experimental results demonstrate that the proposed method can effectively brighten images, suppress image noise and texture loss, and improve the PSNR, SSIM, LOE and NIQE metrics. The proposed method exhibits better performance than other comparison algorithms in subjective and objective evaluation.
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Key words:
- imaging systems /
- image enhancement /
- U-Net /
- wavelet transform
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图 2 基于特征分频的编码器（左）和解码器（右）
Figure 2. Encoder (left) and decoder (right) based on frequency decomposition of features
图 3 Pixel Shuffle和逆Pixel Shuffle方法示意图
Figure 3. Schematic diagram of Pixel Shuffle and inverse Pixel Shuffle method
图 5 LOL数据集的单张图像客观评价结果
Figure 5. Objective evaluation results of single image in LOL dataset
图 6 不同方法在多个数据集上的图像增强结果
Figure 6. Image enhancement results of different methods on multiple datasets
图 7 LOL数据集中的146.jpg图像增强结果：
(a) Low; (b) BPDHE; (c) LIME; (d) MF; (e) Retinexnet; (f) BIMEF; (g) KinD; (h) Ours
Figure 7. Enhancement results of "146.jpg" in LOL dataset:
(a) Low; (b) BPDHE; (c) LIME; (d) MF; (e) Retinexnet; (f) BIMEF; (g) KinD; (h) Ours
图 8 VV数据集中的P1020044.jpg图像增强结果：
(a) Low; (b) BPDHE; (c) LIME; (d) MF; (e) Retinexnet; (f) BIMEF; (g) KinD; (h) Ours
Figure 8. Enhancement results of "p1020044.jpg" in VV dataset:
(a) Low; (b) BPDHE; (c) LIME; (d) MF; (e) Retinexnet; (f) BIMEF; (g) KinD; (h) Ours
图 9 LIME数据集10.bmp图像增强结果：
(a) Low; (b) BPDHE; (c) LIME; (d) MF; (e) Retinexnet; (f) BIMEF; (g) KinD; (h) Ours
Figure 9. Enhancement results of "10.bmp" in LIME dataset:
(a) Low; (b) BPDHE; (c) LIME; (d) MF; (e) Retinexnet; (f) BIMEF; (g) KinD; (h) Ours
图 10 四种不同组合配置的网络训练过程
Figure 10. Training process of 4 networks with different configurations
图 11 真实低光照图像增强结果：其中(a)和(b)为夜晚场景，(c)和(d)为阴雨天场景，(e)和(f)为早晨或傍晚场景
Figure 11. The results of real low-light image enhancement, where (a) and (b) are night scenes, (c) and (d) are overcast and rainy scenes, and (e) and (f) are morning or evening scenes
表 1 不同低光照增强算法在LOL数据集上的结果
Table 1. Results of different low light enhancement algorithms on LOL dataset
Metrics Low BPDHE LIME MF Retinexnet BIMEF KinD Ours PSNR 7.8666 11.8748 16.9332 16.9332 16.9816 13.9722 20.5955 24.5499 SSIM 0.1259 0.3415 0.5644 0.6048 0.5594 0.5771 0.8045 0.8913 LOE 1417.1 1772.8 3207.6 1721.2 4244.1 1553.3 1795.3 1267.1 NIQE 6.7483 7.9852 8.3777 8.8855 8.8779 7.5029 5.3561 5.4289 
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表 2 不同网络的编/解码器和损失函数的配置
Table 2. Configuration of codecs and loss function for different networks
Combination Single-scale L1 Multi-scale L1 Cascaded residual blocks FDF N1 √ - √ - N2 √ - - √ N3 - √ √ - N4 - √ - √
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表 3 不同组合网络的性能比较
Table 3. Performance comparison of different combination networks
Datasets LOL LIME NPE MEF DICM VV Average N1 6.8072 5.0207 6.4623 6.3248 6.5652 6.1562 6.2227 N2 4.8663 4.3757 5.3071 5.4867 5.0311 4.9305 4.9996 N3 4.8091 4.2395 6.2099 6.6436 6.0698 5.8492 5.6369 N4 4.6112 4.2251 4.9695 5.4682 4.9888 4.7840 4.8411
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