NTIRE 2021 Depth Guided Image Relighting Challenge (2021-04-27T00:00:00.000000Z)

TL;DR

This paper reviews the NTIRE 2021 depth guided image relighting challenge and relies on the VIDIT dataset for each of the two challenge tracks, including depth information, to transform the illumination settings of an input image to match those of another guide image, similar to style transfer.

Abstract

Image relighting is attracting increasing interest due to its various applications. From a research perspective, im-age relighting can be exploited to conduct both image normalization for domain adaptation, and also for data augmentation. It also has multiple direct uses for photo montage and aesthetic enhancement. In this paper, we review the NTIRE 2021 depth guided image relighting challenge.We rely on the VIDIT dataset for each of our two challenge tracks, including depth information. The first track is on one-to-one relighting where the goal is to transform the illumination setup of an input image (color temperature and light source position) to the target illumination setup. In the second track, the any-to-any relighting challenge, the objective is to transform the illumination settings of the in-put image to match those of another guide image, similar to style transfer. In both tracks, participants were given depth information about the captured scenes. We had nearly 250 registered participants, leading to 18 confirmed team sub-missions in the final competition stage. The competitions, methods, and final results are presented in this paper.

Authors

S. Süsstrunk

6 papers

Radu Timofte

49 papers

Majed El Helou

6 papers

TL;DR

Abstract

Authors

References59 items

MSR-Net: Multi-Scale Relighting Network for One-to-One Relighting

LTNet: Light Transfer Network for Depth Guided Image Relighting

NTIRE 2021 Challenge on High Dynamic Range Imaging: Dataset, Methods and Results

NTIRE 2021 Challenge on Burst Super-Resolution: Methods and Results

NTIRE 2021 Multi-modal Aerial View Object Classification Challenge

NTIRE 2021 NonHomogeneous Dehazing Challenge Report

NTIRE 2021 Learning the Super-Resolution Space Challenge

NTIRE 2021 Challenge for Defocus Deblurring Using Dual-pixel Images: Methods and Results

NTIRE 2021 Challenge on Perceptual Image Quality Assessment

Physically Inspired Dense Fusion Networks for Relighting

LAFFNet: A Lightweight Adaptive Feature Fusion Network for Underwater Image Enhancement

Multi-modal Bifurcated Network for Depth Guided Image Relighting

S3Net: A Single Stream Structure for Depth Guided Image Relighting

NTIRE 2021 Challenge on Video Super-Resolution

NTIRE 2021 Challenge on Image Deblurring

NTIRE 2021 Challenge on Quality Enhancement of Compressed Video: Methods and Results

Multi-scale Self-calibrated Network for Image Light Source Transfer

Dsrn: an Efficient Deep Network for Image Relighting

Degradation Aware Approach to Image Restoration Using Knowledge Distillation

AIM 2020: Scene Relighting and Illumination Estimation Challenge

WDRN : A Wavelet Decomposed RelightNet for Image Relighting

Modified color CCD moiré method and its application in optical distortion correction

Deep Relighting Networks for Image Light Source Manipulation

Progressively Guided Alternate Refinement Network for RGB-D Salient Object Detection

Hierarchical Dynamic Filtering Network for RGB-D Salient Object Detection

2D Image Relighting with Image-to-Image Translation

Scene relighting with illumination estimation in the latent space on an encoder-decoder scheme

PMHLD: Patch Map-Based Hybrid Learning DehazeNet for Single Image Haze Removal

VIDIT: Virtual Image Dataset for Illumination Transfer

Hierarchical Regression Network for Spectral Reconstruction from RGB Images

Simultaneous Demosaicing and Chromatic Aberration Correction through Spectral Reconstruction

Spatially-Attentive Patch-Hierarchical Network for Adaptive Motion Deblurring

Y-Net: Multi-Scale Feature Aggregation Network With Wavelet Structure Similarity Loss Function For Single Image Dehazing

Stochastic Frequency Masking to Improve Super-Resolution and Denoising Networks

Replacing Mobile Camera ISP with a Single Deep Learning Model

Depth-Guided Dense Dynamic Filtering Network for Bokeh Effect Rendering

Deep Single-Image Portrait Relighting

Blind Universal Bayesian Image Denoising With Gaussian Noise Level Learning

Enhanced Pix2pix Dehazing Network

PMS-Net: Robust Haze Removal Based on Patch Map for Single Images

Res2Net: A New Multi-Scale Backbone Architecture

A comparative study on wavelets and residuals in deep super resolution

AAM: AN Assessment Metric of Axial Chromatic Aberration

ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design

The Unreasonable Effectiveness of Deep Features as a Perceptual Metric

Correlation-based deblurring leveraging multispectral chromatic aberration in color and near-infrared joint acquisition

A General and Adaptive Robust Loss Function

FuseNet: Incorporating Depth into Semantic Segmentation via Fusion-Based CNN Architecture

Single Image Dehazing via Multi-scale Convolutional Neural Networks

Densely Connected Convolutional Networks

Perceptual Losses for Real-Time Style Transfer and Super-Resolution

U-Net: Convolutional Networks for Biomedical Image Segmentation

Image quality assessment: from error visibility to structural similarity

The rendering equation

An Ensemble Neural Network for Scene Relighting with Light Classification

JSTASR: Joint Size and Transparency-Aware Snow Removal Algorithm Based on Modified Partial Convolution and Veiling Effect Removal

SA-AE for Any-to-Any Relighting

Medical Image Computing and Computer-Assisted Intervention -MICCAI

Real-time single image and video super-resolution using an efficient subpixel convolutional neural network

Field of Study

Journal Information

Name

Page