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computer-vision-5

highlight removal

3260 papers • 126 benchmarks • 313 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.

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Most implemented papers

Single Image Highlight Removal with a Sparse and Low-Rank Reflection Model

Limin Wang, Jie Guo, Zuojian Zhou•Fri Sep 07 2018

A sparse and low-rank reflection model for specular highlight detection and removal using a single input image that is competitive with previous methods, especially in some challenging scenarios featuring natural illumination, hue-saturation ambiguity and strong noises.

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Polarization Guided Specular Reflection Separation

Feng Lu, Sijia Wen, Yingqiang Zheng•Sun Mar 21 2021

A polarization guided model is derived to incorporate the polarization information into a designed iteration optimization separation strategy to separate the specular reflection and is proposed 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.

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A Multi-Task Network for Joint Specular Highlight Detection and Removal

Gang Fu, Qing Zhang, Chunxia Xiao, Lei Zhu, Ping Li•Mon May 31 2021

Specular highlight detection and removal are fundamental and challenging tasks. Although recent methods have achieved promising results on the two tasks by training on synthetic training data in a supervised manner, they are typically solely designed for highlight detection or removal, and their performance usually deteriorates significantly on real-world images. In this paper, we present a novel network that aims to detect and remove highlights from natural images. To remove the domain gap between synthetic training samples and real test images, and support the investigation of learning-based approaches, we first introduce a dataset with about 16K real images, each of which has the corresponding ground truths of highlight detection and removal. Using the presented dataset, we develop a multi-task network for joint highlight detection and removal, based on a new specular highlight image formation model. Experiments on the benchmark datasets and our new dataset show that our approach clearly outperforms state-of-the-art methods for both highlight detection and removal.

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Text-Aware Single Image Specular Highlight Removal

Shiyu Hou, Chaoqun Wang, Weize Quan, J. Jiang, Dong‐Ming Yan•Sun Aug 15 2021

The core goal is to improve the accuracy of text detection and recognition by removing the highlight from text images by designing a novel two-stage network, which contains a highlight detection network and a highlight removal network.

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Single-Image Specular Highlight Removal via Real-World Dataset Construction

Jianwei Guo, Xiaopeng Zhang, Dongming Yan, Zhongqi Wu, Chuanqing Zhuang, Jian Shi, Jun Xiao•Thu Aug 26 2021

Specular reflections pose great challenges on various multimedia and computer vision tasks, e.g., image segmentation, detection and matching. In this paper, we build a large-scale Paired Specular-Diffuse (PSD) image dataset, where the images are carefully captured by using real-world objects and the ground-truth specular-free diffuse images are provided. To the best of our knowledge, this is the first real-world benchmark dataset for specular highlight removal task, which is useful for evaluating and encouraging new deep learning-based approaches. Given this dataset, we present a novel Generative Adversarial Network (GAN) for specular highlight removal from a single image by introducing the detection of specular reflection information as a guidance. Our network also makes full use of the attention mechanism and is able to directly model the mapping relation between the diffuse area and the specular highlight area without any explicit estimation of the illumination. Experimental results demonstrate that the proposed network is more effective to remove specular reflection components with the guidance of specular highlight detection than recent state-of-the-art methods.

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M2-Net: Multi-stages Specular Highlight Detection and Removal in Multi-scenes

Zhaoyang Huang, Kun Hu, Xingjun Wang•Tue Jul 19 2022

Extensive experimental results indicate that the proposed framework can obtain excellent visual effects of highlight removal and achieve state-of-the-art results in several quantitative evaluation metrics.

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Estimating Reflectance Layer from A Single Image: Integrating Reflectance Guidance and Shadow/Specular Aware Learning

R. Tan, Wenhan Yang, Yeying Jin, Ruoteng Li•Sat Nov 26 2022

Estimating the reflectance layer from a single image is a challenging task. It becomes more challenging when the input image contains shadows or specular highlights, which often render an inaccurate estimate of the reflectance layer. Therefore, we propose a two-stage learning method, including reflectance guidance and a Shadow/Specular-Aware (S-Aware) network to tackle the problem. In the first stage, an initial reflectance layer free from shadows and specularities is obtained with the constraint of novel losses that are guided by prior-based shadow-free and specular-free images. 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. Our network employs a classifier to categorize shadow/shadow-free, specular/specular-free classes, enabling the activation features to function as attention maps that focus on shadow/specular regions. Our quantitative and qualitative evaluations show that our method outperforms the state-of-the-art methods in the reflectance layer estimation that is free from shadows and specularities.

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Towards High-Quality Specular Highlight Removal by Leveraging Large-Scale Synthetic Data

Lei Zhu, Gang Fu, Chunxia Xiao, Qing Zhang, Ping Li•Mon Sep 11 2023

A three-stage network is proposed to remove specular highlights from a single object-level image, able to generalize well to unseen real object-level images, and even produce good results for scene-level images with multiple background objects and complex lighting.

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Dual-Hybrid Attention Network for Specular Highlight Removal

Xuhang Chen, Shuqiang Wang, Shenghong Luo, Xiaojiao Guo, Chi-Man Pun•Tue Jul 16 2024

It is demonstrated that DHAN-SHR outperforms 18 state-of-the-art methods both quantitatively and qualitatively, setting a new standard for specular highlight removal in multimedia applications.

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highlight removal | State-of-the-Art