具有光照曲线的非成对强光图像增强网络

Fengyu Yang
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引用次数: 0

摘要

图像是人类获取信息的一种方式,在我们的日常生活中无处不在。与其他媒体相比,我们更倾向于通过图像来获取信息。目前大多数图像增强方法都是基于视网膜理论的。然而,这种方法容易导致图像信息的丢失。更重要的是,该理论是基于光平滑变化的假设。这个假设对明亮的图像非常不友好。为了解决这一问题,提出了一种强光图像的不配对增强网络。网络由照度曲线、照度损失函数和照度衰减网络组成。实验表明,我们的网络可以明显改善强光环境造成的图像对比度,显著提高强光图像的质量。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Unpaired strong-light image augmentation network with illumination curve
Images are a way for humans to get information, and they are ubiquitous in our daily lives. Compared to other media, we tend to get information through images. Most current image enhancement methods are based on Retinex theory. However, this method can easily lead to the loss of image information. More importantly, the theory is based on the assumption that light changes smoothly. And this assumption is very unfriendly to bright images. To solve this problem, an unpaired enhancement network for strong light image is proposed. The network consists of illumination curve, illumination loss function and illumination attenuation network. Experiments show that our network can obviously improve the image contrast caused by strong light environment, and significantly improve the quality of strong light image.
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