Intrinsic Image Transfer for Illumination Manipulation (2021-07-01T00:00:00.000000Z)

TL;DR

A novel intrinsic image transfer (IIT) algorithm for image illumination manipulation, which creates a local image translation between two illumination surfaces, built on an optimization-based framework composed of illumination, reflectance and content photo-realistic losses.

Abstract

This article presents a novel intrinsic image transfer (IIT) algorithm for image illumination manipulation, which creates a local image translation between two illumination surfaces. This model is built on an optimization-based framework composed of illumination, reflectance and content photo-realistic losses, respectively. Each loss is first defined on the corresponding sub-layers factorized by an intrinsic image decomposition and then reduced under the well-known spatial-varying illumination illumination-invariant reflectance prior knowledge. We illustrate that all losses, with the aid of an “exemplar” image, can be directly defined on images without the necessity of taking an intrinsic image decomposition, thereby giving a closed-form solution to image illumination manipulation. We also demonstrate its versatility and benefits to several illumination-related tasks: illumination compensation, image enhancement and tone mapping, and high dynamic range (HDR) image compression, and show their high-quality results on natural image datasets.

Authors

Junqing Huang

1 papers

Michael Ruzhansky

1 papers

Qianying Zhang

1 papers

TL;DR

Abstract

Authors

References76 items

STAR: A Structure-aware Lightweight Transformer for Real-time Image Enhancement

Benchmarking Low-Light Image Enhancement and Beyond

Zero-Reference Deep Curve Estimation for Low-Light Image Enhancement

EnlightenGAN: Deep Light Enhancement Without Paired Supervision

A Closed-Form Solution to Universal Style Transfer

Photorealistic Style Transfer via Wavelet Transforms

Deep Retinex Decomposition for Low-Light Enhancement

Deep Photo Enhancer: Unpaired Learning for Image Enhancement from Photographs with GANs

Intrinsic Image Transformation via Scale Space Decomposition

Learning to See in the Dark

Learning Intrinsic Image Decomposition from Watching the World

Multispectral Image Intrinsic Decomposition via Low Rank Constraint

LIME: Live Intrinsic Material Estimation

Self-Supervised Intrinsic Image Decomposition

NIMA: Neural Image Assessment

WESPE: Weakly Supervised Photo Enhancer for Digital Cameras

Universal Style Transfer via Feature Transforms

Neural Style Transfer: A Review

DSLR-Quality Photos on Mobile Devices with Deep Convolutional Networks

Learning to predict indoor illumination from a single image

Unpaired Image-to-Image Translation Using Cycle-Consistent Adversarial Networks

Arbitrary Style Transfer in Real-Time with Adaptive Instance Normalization

LIME: Low-Light Image Enhancement via Illumination Map Estimation

Deep Outdoor Illumination Estimation

Image Style Transfer Using Convolutional Neural Networks

A Weighted Variational Model for Simultaneous Reflectance and Illumination Estimation

The Cityscapes Dataset for Semantic Urban Scene Understanding

Radiometric Scene Decomposition: Scene Reflectance, Illumination, and Geometry from RGB-D Images

Direct Intrinsics: Learning Albedo-Shading Decomposition by Convolutional Regression

An L1 image transform for edge-preserving smoothing and scene-level intrinsic decomposition

High Dynamic Range Imaging

Illumination and reflectance spectra separation of a hyperspectral image meets low-rank matrix factorization

A Feature-Enriched Completely Blind Image Quality Evaluator

Non-Local Retinex - A Unifying Framework and Beyond

Intrinsic Image Decomposition Using Structure-Texture Separation and Surface Normals

Intrinsic images in the wild

Vision meets robotics: The KITTI dataset

Efficient preconditioning of laplacian matrices for computer graphics

Objective Quality Assessment of Tone-Mapped Images

ImageNet classification with deep convolutional neural networks

A Naturalistic Open Source Movie for Optical Flow Evaluation

A Closed-Form Solution to Retinex with Nonlocal Texture Constraints

Recovering Intrinsic Images with a Global Sparsity Prior on Reflectance

Pose, illumination and expression invariant pairwise face-similarity measure via Doppelgänger list comparison

Intrinsic images decomposition using a local and global sparse representation of reflectance

An L1-based variational model for Retinex theory and its application to medical images

A Total Variation Model for Retinex

A PDE Formalization of Retinex Theory

MLLE: Modified Locally Linear Embedding Using Multiple Weights

Retinex by Two Bilateral Filters

Image quality assessment: from error visibility to structural similarity

Iterative methods for sparse linear systems

Gradient domain high dynamic range compression

Photographic tone reproduction for digital images

A Variational Framework for Retinex

Color Transfer between Images

The Fundamentals of Gamut Mapping: A Survey

Nonlinear dimensionality reduction by locally linear embedding.

Bilateral filtering for gray and color images

A multiscale retinex for bridging the gap between color images and the human observation of scenes

Contrast Limited Adaptive Histogram Equalization

Recent advances in retinex theory and some implications for cortical computations: color vision and the natural image.

The retinex theory of color vision.

Adobe Photoshop CC

GENERATIVE ADVERSARIAL NETS

He was awarded by FWO (Belgium) the prestigious Odysseus 1 Project

A Tour of Modern Image Filtering: New Insights and Methods, Both Practical and Theoretical

Seeing in Black and White

Determining Lightness from an Image

Think Globally, Fit Locally: Unsupervised Learning of Low Dimensional Manifold

Recovering intrinsic scene characteristics

Lightness and retinex theory.

Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence Learning Linear and Kernel Predictors with the 0–1 Loss Function

“Nasa retinex,”

Ieee Transactions on Pattern Analysis and Machine Intelligence Shape, Illumination, and Reflectance from Shading

Google nikcollection