论文标题
wdrn：小波分解的重新网络用于图像重新启动
WDRN : A Wavelet Decomposed RelightNet for Image Relighting
论文作者
论文摘要
将图像中的照明设置重新校准为目标配置的任务称为重新设置。重新计算技术在数字摄影，游戏行业和增强现实中具有潜在的应用。在本文中，我们解决了一个一对一的重新确认问题，其中预测目标照明设置的图像具有特定照明条件的输入图像。为此，我们提出了一个称为WDRN的小波分解的RelightNet，它是一种新型的编码器折叠网络，该网络采用基于小波的分解，然后在Muti-Jolution框架下进行卷积层。我们还提出了一种称为灰色损失的新型损失功能，该功能可确保沿着地面真相图像的不同方向在照明中有效学习梯度，从而产生了视觉上较高的重新放置图像。拟议的解决方案在图像操作（AIM）2020研讨会上赢得了重新挑战事件的第一个位置，该工作室证明了其有效性是根据平均感知得分来衡量的，这又是使用SSIM和学习的知觉图像贴片相似性得分来衡量的。
The task of recalibrating the illumination settings in an image to a target configuration is known as relighting. Relighting techniques have potential applications in digital photography, gaming industry and in augmented reality. In this paper, we address the one-to-one relighting problem where an image at a target illumination settings is predicted given an input image with specific illumination conditions. To this end, we propose a wavelet decomposed RelightNet called WDRN which is a novel encoder-decoder network employing wavelet based decomposition followed by convolution layers under a muti-resolution framework. We also propose a novel loss function called gray loss that ensures efficient learning of gradient in illumination along different directions of the ground truth image giving rise to visually superior relit images. The proposed solution won the first position in the relighting challenge event in advances in image manipulation (AIM) 2020 workshop which proves its effectiveness measured in terms of a Mean Perceptual Score which in turn is measured using SSIM and a Learned Perceptual Image Patch Similarity score.
