# WDRN : A Wavelet Decomposed RelightNet for Image Relighting

arXiv: https://arxiv.org/abs/2009.06678 · HTML: https://mlsanity.com/paper/2009.06678/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Sep 2020.

## Built on by (most cited first, 14 of 14)

- [WaveDM: Wavelet-Based Diffusion Models for Image Restoration](https://mlsanity.com/paper/2305.13819/) (2023)
- [NTIRE 2021 Depth Guided Image Relighting Challenge](https://mlsanity.com/paper/2104.13365/) (2021)
- [S3Net: A Single Stream Structure for Depth Guided Image Relighting](https://mlsanity.com/paper/2105.00681/) (2021)
- [Multi-modal Bifurcated Network for Depth Guided Image Relighting](https://mlsanity.com/paper/2105.00690/) (2021)
- [Specularity Factorization for Low-Light Enhancement](https://mlsanity.com/paper/2404.01998/) (2024)
- [Relighting from a Single Image: Datasets and Deep Intrinsic-based Architecture](https://mlsanity.com/paper/2409.18770/) (2024)
- [Multi-scale Self-calibrated Network for Image Light Source Transfer](https://mlsanity.com/paper/2104.08838/) (2021)
- [SILT: Self-supervised Lighting Transfer Using Implicit Image Decomposition](https://mlsanity.com/paper/2110.12914/) (2021)
- [Physically Inspired Dense Fusion Networks for Relighting](https://mlsanity.com/paper/2105.02209/) (2021)
- [Designing An Illumination-Aware Network for Deep Image Relighting](https://mlsanity.com/paper/2207.10582/) (2022)
- [SIRfyN: Single Image Relighting from your Neighbors](https://mlsanity.com/paper/2112.04497/) (2021)
- [MLI-NeRF: Multi-Light Intrinsic-Aware Neural Radiance Fields](https://mlsanity.com/paper/2411.17235/) (2024)
- [Learning Relighting and Intrinsic Decomposition in Neural Radiance Fields](https://mlsanity.com/paper/2406.11077/) (2024)
- [A super-resolution reconstruction method for lightweight building images based on an expanding feature modulation network](https://mlsanity.com/paper/2503.13179/) (2025)

## Abstract (arXiv)

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.