Zhen Tong, Peng Daxin, Li Zhihui
College of Information Science and Engineering, Henan University of Technology, Zhengzhou 450001, China.
Key Laboratory of Grain Information Processing and Control, Ministry of Education, Henan University of Technology, Zhengzhou 450001, China.
Sensors (Basel). 2023 Aug 7;23(15):6990. doi: 10.3390/s23156990.
Images captured under complex conditions frequently have low quality, and image performance obtained under low-light conditions is poor and does not satisfy subsequent engineering processing. The goal of low-light image enhancement is to restore low-light images to normal illumination levels. Although many methods have emerged in this field, they are inadequate for dealing with noise, color deviation, and exposure issues. To address these issues, we present CGAAN, a new unsupervised generative adversarial network that combines a new attention module and a new normalization function based on cycle generative adversarial networks and employs a global-local discriminator trained with unpaired low-light and normal-light images and stylized region loss. Our attention generates feature maps via global and average pooling, and the weights of different feature maps are calculated by multiplying learnable parameters and feature maps in the appropriate order. These weights indicate the significance of corresponding features. Specifically, our attention is a feature map attention mechanism that improves the network's feature-extraction ability by distinguishing the normal light domain from the low-light domain to obtain an attention map to solve the color bias and exposure problems. The style region loss guides the network to more effectively eliminate the effects of noise. The new normalization function we present preserves more semantic information while normalizing the image, which can guide the model to recover more details and improve image quality even further. The experimental results demonstrate that the proposed method can produce good results that are useful for practical applications.
在复杂条件下拍摄的图像质量往往较低，在低光照条件下获得的图像性能较差，无法满足后续工程处理的要求。低光照图像增强的目标是将低光照图像恢复到正常光照水平。尽管该领域已经出现了许多方法，但它们在处理噪声、颜色偏差和曝光问题方面仍存在不足。为了解决这些问题，我们提出了CGAAN，这是一种新的无监督生成对抗网络，它基于循环生成对抗网络，结合了一个新的注意力模块和一个新的归一化函数，并采用了一个由未配对的低光照和正常光照图像训练的全局-局部判别器以及风格化区域损失。我们的注意力通过全局池化和平均池化生成特征图，并通过以适当顺序将可学习参数与特征图相乘来计算不同特征图的权重。这些权重表示相应特征的重要性。具体而言，我们的注意力是一种特征图注意力机制，通过区分正常光照域和低光照域来提高网络的特征提取能力，从而获得一个注意力图来解决颜色偏差和曝光问题。风格化区域损失引导网络更有效地消除噪声的影响。我们提出的新归一化函数在对图像进行归一化的同时保留了更多语义信息，这可以引导模型恢复更多细节并进一步提高图像质量。实验结果表明，所提出的方法能够产生良好的结果，对实际应用具有重要价值。
