| 摘要: |
| 为了解决低光照图像存在的对比度低、噪声大等问题,提出一种基于 Retinex 理论的卷积神经网络增强模型
(Retinex-RANet)。 它包括分解网络、降噪网络和亮度调整网络 3 部分:在分解网络中融入残差模块(RB)和跳跃连
接,通过跳跃连接将第一个卷积层提取的特征与每一个 RB 提取的特征融合,以确保图像特征的完整提取,从而得
到更准确的反射分量和光照分量;降噪网络以 U-Net 网络为基础,同时加入了空洞卷积和注意力机制,空洞卷积能
提取更多的图像相关信息,注意力机制可以更好地去除反射分量中噪声,还原细节;亮度调整网络由卷积层和
Sigmoid 层组成,用来提高光照分量的对比度;最后将降噪网络去噪后的反射分量和亮度调整网络增强后的光照分
量融合,得到最终的增强结果。 实验结果显示:Retinex-RANet 在主观视觉上不仅提高了低光图像的亮度,还提高了色彩深度和对比度,在客观评价指标上,相较于 R2RNet,PSNR 值上升了 4. 4%,SSIM 值上升了 6. 1%。 结果表
明:Retinex-RANet 具有更好的低光图像增强效果 |
| 关键词: 低光增强 残差模块 注意力机制 Retinex 理论 |
| DOI： |
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SHI Yufei，ZHAO Baiting
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School of Electrical and Information Engineering， Anhui University of Science and Technology， Anhui Huainan 232001，
China
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| Abstract: |
| In order to solve the problems of low contrast and high noise of low-light images a convolutional neural network
enhancement model based on Retinex theory Retinex-RANet is proposed. It includes three parts the decomposition
network the noise reduction network and the brightness adjustment network. The residual module RB and the jump
connection were incorporated into the decomposition network and the features extracted by the first convolutional layer
were fused with each RB extracted feature through the jump connection to ensure the complete extraction of the image
features resulting in more accurate reflection and illumination components. The noise reduction network was based on the
U-Net network and the cavity convolution and attention mechanism were added at the same time so as to extract more
image-related information. The attention mechanism can better remove the noise in the reflected component and restore the
details. The brightness adjustment network consists of a convolutional layer and a Sigmoid layer which is used to increase
the contrast of the light components. Finally the reflection component after denoising by the noise reduction network and
the light component after the brightness adjustment network were fused to obtain the final enhancement result.
Experimental results show that Retinex-RANet not only improves the brightness of low-light images in subjective vision
but also improves the color depth and contrast. In terms of objective evaluation indicators compared with R2RNet the
PSNR value increased by 4. 4% and the SSIM value increased by 6. 1%. The results show that Retinex-RANet has better low-light image enhancement. |
| Key words: low light enhancement residuals module attention mechanism Retinex theory |
