基于改进循环生成对抗网络的低照度图像增强

为了解决在低照度图像增强过程中配对数据集获取困难,且经过增强后的图像质量不佳的问题,通过改进循环生成对抗网络的方法研究了非配对低照度图像增强的实现。主要方法为改进生成器和判别器的结构提高增强后图像的质量。生成器部分采用融合了Vision Transformer结构的U-NET模型替代原始的生成器模型,来提高图像变换的周期一致性和内容保持性,并有效的处理图像研究中普遍存在的长距离空间相关性的问题。判别器部分针对图像研究的特点选择PatchGAN代替传统的判别器,提高对图像细节的判别能力,提高图像质量。结果表明,相比较于传统方法,本文改进的模型有着更好的主观视觉效果,同时在客观评价指标也有着相应的提高。可见本文改进模型的有效性。

Low-light image enhancement based on improved cycle generative adversarial network

In order to solve the problem that paired-based datasets are difficult to obtain during low-illumination image enhancement and the image quality after enhancement is poor, the implementation of unpaired low-illumination image enhancement is studied by improving the cyclic generative adversarial network. The main method is to improve the structure of the generator and discriminator and improve the quality of the enhanced image. The generator part uses the U-NET model integrating the Vision Transformer structure to replace the original generator model, which improves the periodic consistency and content retention of image transformation, and effectively deals with the long-distance spatial correlation problems common in image research. In view of the characteristics of image research, PatchGAN is selected instead of the traditional discriminator to improve the ability to discriminate image details and improve image quality. The results show that compared with the traditional method, the improved model has better subjective visual effects, and the objective evaluation index is also improved. It is concluded that the effectiveness of the model improvement in this paper is displayed.