Abstract
In this paper, we propose a novel convolutional neural network (CNN) architecture considering both local and global features for image enhancement. Most conventional image enhancement methods, including Retinex-based methods, cannot restore lost pixel values caused by clipping and quantizing. CNN-based methods have recently been proposed to solve the problem, but they still have a limited performance due to network architectures not handling global features. To handle both local and global features, the proposed architecture consists of three networks: a local encoder, a global encoder, and a decoder. In addition, high dynamic range (HDR) images are used for generating training data for our networks. The use of HDR images makes it possible to train CNNs with better-quality images than images directly captured with cameras. Experimental results show that the proposed method can produce higher-quality images than conventional image enhancement methods including CNN-based methods, in terms of various objective quality metrics: TMQI, entropy, NIQE, and BRISQUE.
Abstract (translated)
本文提出了一种考虑局部和全局特征的卷积神经网络（CNN）图像增强结构。大多数传统的图像增强方法，包括基于retinex的方法，都无法恢复由剪切和量化造成的丢失像素值。基于CNN的方法最近已经被提出来解决这个问题，但是由于网络架构不处理全局特性，它们的性能仍然有限。为了同时处理本地和全局特性，所提出的体系结构由三个网络组成：本地编码器、全局编码器和解码器。此外，高动态范围（HDR）图像用于为我们的网络生成训练数据。HDR图像的使用使培训CNN的图像质量比直接用相机拍摄的图像更好成为可能。实验结果表明，与传统的基于CNN的图像增强方法相比，该方法可以在不同的客观质量指标上产生更高的图像质量：tmqi、熵、niqe和brisque。
URL
https://arxiv.org/abs/1905.02899
