  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.
