highlight removal
9 papers with code • 0 benchmarks • 0 datasets
Highlight removal refers to the process of eliminating or reducing the presence of specular highlights in an image. Specular highlights are bright spots or reflections that occur when light reflects off a shiny or reflective surface, such as glass, metal, or oily skin. These highlights can often obscure or distort the underlying details of the image, making it difficult to analyze or process.
Benchmarks
These leaderboards are used to track progress in highlight removal
Most implemented papers
Single Image Highlight Removal with a Sparse and Low-Rank Reflection Model
dingguanglei/SLRR-SparseAndLowRankReflectionModel
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ECCV 2018
We propose a sparse and low-rank reflection model for specular highlight detection and removal using a single input image.
Polarization Guided Specular Reflection Separation
Based on the analysis of polarization, we propose a polarization guided model to generate a polarization chromaticity image, which is able to reveal the geometrical profile of the input image in complex scenarios, such as diversity of illumination.
A Multi-Task Network for Joint Specular Highlight Detection and Removal
Specular highlight detection and removal are fundamental and challenging tasks.
Text-Aware Single Image Specular Highlight Removal
The core goal is to improve the accuracy of text detection and recognition by removing the highlight from text images.
Single-Image Specular Highlight Removal via Real-World Dataset Construction
jianweiguo/SpecularityNet-PSD
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TMM 2021
Specular reflections pose great challenges on various multimedia and computer vision tasks, e. g. , image segmentation, detection and matching.
M2-Net: Multi-stages Specular Highlight Detection and Removal in Multi-scenes
The framework consists of three main components, highlight feature extractor module, highlight coarse removal module, and highlight refine removal module.
Estimating Reflectance Layer from A Single Image: Integrating Reflectance Guidance and Shadow/Specular Aware Learning
To further enforce the reflectance layer to be independent of shadows and specularities in the second-stage refinement, we introduce an S-Aware network that distinguishes the reflectance image from the input image.
Towards High-Quality Specular Highlight Removal by Leveraging Large-Scale Synthetic Data
fu123456/TSHRNet
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ICCV 2023
This paper aims to remove specular highlights from a single object-level image.
Joint network for specular highlight detection and adversarial generation of specular-free images trained with polarimetric data
Atif-Anwer/SHMGAN
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Neurocomputing 2023
Once trained, SHMGAN is able to generate specular-free images from a single RGB image as input; without requiring any additional external labels.
