Image Enhancement Network Trained by Using HDR images
This addresses image quality restoration for applications like photography or computer vision, but it is incremental as it builds on existing CNN-based approaches.
The paper tackles image enhancement by using HDR images to generate training data for a network, resulting in higher-quality images with improved TMQI and NIQE scores compared to conventional methods.
In this paper, a novel image enhancement network is proposed, where HDR images are used for generating training data for our network. Most of conventional image enhancement methods, including Retinex based methods, do not take into account restoring lost pixel values caused by clipping and quantizing. In addition, recently proposed CNN based methods still have a limited scope of application or a limited performance, due to network architectures. In contrast, the proposed method have a higher performance and a simpler network architecture than existing CNN based methods. Moreover, the proposed method enables us to restore lost pixel values. Experimental results show that the proposed method can provides higher-quality images than conventional image enhancement methods including a CNN based method, in terms of TMQI and NIQE.