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.

本文介绍了一种基于小波分解的编码器 - 解码器网络，命名为 WDRN，用于解决一对一的照明重新校准问题，提出了一种名为 gray loss 的新型损失函数以保证对地真图像中照明的梯度在不同方向上的高效学习，该方案在 AIM2020 工作坊的 relighting 挑战活动中获得了第一名。

WDRN：一种用于图像照明的小波分解补光网络

WDRN : A Wavelet Decomposed RelightNet for Image Relighting

In digital photography, two image restoration tasks have been studied
extensively and resolved independently: demosaicing and super-resolution. Both
these tasks are related to resolution limitations of the camera. Performing
super-resolution on a demosaiced images simply exacerbates the artifacts
introduced by demosaicing. In this paper, we show that such accumulation of
errors can be easily averted by jointly performing demosaicing and
super-resolution. To this end, we propose a deep residual network for learning
an end-to-end mapping between Bayer images and high-resolution images. By
training on high-quality samples, our deep residual demosaicing and
super-resolution network is able to recover high-quality super-resolved images
from low-resolution Bayer mosaics in a single step without producing the
artifacts common to such processing when the two operations are done
separately. We perform extensive experiments to show that our deep residual
network achieves demosaiced and super-resolved images that are superior to the
state-of-the-art both qualitatively and in terms of PSNR and SSIM metrics.

本论文提出了一种使用深度残差网络联合执行 Bayer 图像去马赛克和超分辨率的方法，按照高质量样本训练的模型可在单步骤中从低分辨率 Bayer 马赛克图像中恢复高质量的超分辨率图像。实验表明，该方法优于目前的各种技术，并在定量和定性指标上取得了优异的表现。