Computers & Graphics-Uk最新文献

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Multi-scale wavelet feature fusion network for low-light image enhancement

IF 2.5 4区计算机科学 Q2 COMPUTER SCIENCE, SOFTWARE ENGINEERING

Computers & Graphics-Uk

Pub Date : 2025-02-21 DOI: 10.1016/j.cag.2025.104182

Ran Wei , Xinjie Wei , Shucheng Xia , Kan Chang , Mingyang Ling , Jingxiang Nong , Li Xu

Low-light image enhancement (LLIE) aims to enhance the visibility and quality of low-light images. However, existing methods often struggle to effectively balance global and local image content, resulting in suboptimal results. To address this challenge, we propose a novel multi-scale wavelet feature fusion network (MWFFnet) for low-light image enhancement. Our approach utilizes a U-shaped architecture where traditional downsampling and upsampling operations are replaced by discrete wavelet transform (DWT) and inverse DWT (IDWT), respectively. This strategy helps to reduce the difficulty of learning the complex mapping from low-light images to well-exposed ones. Furthermore, we incorporate a dual transposed attention (DTA) module for each feature scale. DTA effectively captures long-range dependencies between image contents, thus enhancing the network’s ability to understand intricate image structures. To further improve the enhancement quality, we develop a cross-layer attentional feature fusion (CAFF) module that effectively integrates features from both the encoder and decoder. This mechanism enables the network to leverage contextual information across various levels of representation, resulting in a more comprehensive understanding of the images. Extensive experiments demonstrate that with a reasonable model size, the proposed MWFFnet outperforms several state-of-the-art methods. Our code will be available online.²

{"title":"Multi-scale wavelet feature fusion network for low-light image enhancement","authors":"Ran Wei , Xinjie Wei , Shucheng Xia , Kan Chang , Mingyang Ling , Jingxiang Nong , Li Xu","doi":"10.1016/j.cag.2025.104182","DOIUrl":"10.1016/j.cag.2025.104182","url":null,"abstract":"<div><div>Low-light image enhancement (LLIE) aims to enhance the visibility and quality of low-light images. However, existing methods often struggle to effectively balance global and local image content, resulting in suboptimal results. To address this challenge, we propose a novel multi-scale wavelet feature fusion network (MWFFnet) for low-light image enhancement. Our approach utilizes a U-shaped architecture where traditional downsampling and upsampling operations are replaced by discrete wavelet transform (DWT) and inverse DWT (IDWT), respectively. This strategy helps to reduce the difficulty of learning the complex mapping from low-light images to well-exposed ones. Furthermore, we incorporate a dual transposed attention (DTA) module for each feature scale. DTA effectively captures long-range dependencies between image contents, thus enhancing the network’s ability to understand intricate image structures. To further improve the enhancement quality, we develop a cross-layer attentional feature fusion (CAFF) module that effectively integrates features from both the encoder and decoder. This mechanism enables the network to leverage contextual information across various levels of representation, resulting in a more comprehensive understanding of the images. Extensive experiments demonstrate that with a reasonable model size, the proposed MWFFnet outperforms several state-of-the-art methods. Our code will be available online.<span><span><sup>2</sup></span></span></div></div>","PeriodicalId":50628,"journal":{"name":"Computers & Graphics-Uk","volume":"127 ","pages":"Article 104182"},"PeriodicalIF":2.5,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143473939","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Visual comfort and depth perception measurement for stereoscopic image retargeting quality assessment

IF 2.5 4区计算机科学 Q2 COMPUTER SCIENCE, SOFTWARE ENGINEERING

Computers & Graphics-Uk

Pub Date : 2025-02-20 DOI: 10.1016/j.cag.2025.104179

Zhenhua Tang, Yin Zhang, Xuejun Zhang

Most stereoscopic image retargeting quality assessment (SIRQA) algorithms ignore the binocular difference between the left and right views on visually important content and the relative depth difference between the original and resized images, lowering the performance of the SIRQA algorithms. To address these issues, we propose a metric to measure the visual comfort of stereoscopic retargeted images, which assesses the binocular inconsistency caused by the difference between the left and right views in terms of the matched pixel pairs and information loss in salient regions. We also present a metric to evaluate the depth perception distortion of stereoscopic retargeted images, which calculates the relative depth between the background and the foreground objects in the original and the retargeted image respectively, and measures the relative depth difference between the original and the resized photos. Furthermore, we adopt the two proposed metrics to a SIRQA framework based on image classification to perform the quality evaluation of the stereoscopic resized images with other metrics. Experimental results demonstrate that the performance of the proposed SIRQA method outperforms the state-of-the-art algorithms. Moreover, ablation studies indicate that the proposed metrics can effectively improve the consistency between subjective and objective evaluations.

{"title":"Visual comfort and depth perception measurement for stereoscopic image retargeting quality assessment","authors":"Zhenhua Tang, Yin Zhang, Xuejun Zhang","doi":"10.1016/j.cag.2025.104179","DOIUrl":"10.1016/j.cag.2025.104179","url":null,"abstract":"<div><div>Most stereoscopic image retargeting quality assessment (SIRQA) algorithms ignore the binocular difference between the left and right views on visually important content and the relative depth difference between the original and resized images, lowering the performance of the SIRQA algorithms. To address these issues, we propose a metric to measure the visual comfort of stereoscopic retargeted images, which assesses the binocular inconsistency caused by the difference between the left and right views in terms of the matched pixel pairs and information loss in salient regions. We also present a metric to evaluate the depth perception distortion of stereoscopic retargeted images, which calculates the relative depth between the background and the foreground objects in the original and the retargeted image respectively, and measures the relative depth difference between the original and the resized photos. Furthermore, we adopt the two proposed metrics to a SIRQA framework based on image classification to perform the quality evaluation of the stereoscopic resized images with other metrics. Experimental results demonstrate that the performance of the proposed SIRQA method outperforms the state-of-the-art algorithms. Moreover, ablation studies indicate that the proposed metrics can effectively improve the consistency between subjective and objective evaluations.</div></div>","PeriodicalId":50628,"journal":{"name":"Computers & Graphics-Uk","volume":"127 ","pages":"Article 104179"},"PeriodicalIF":2.5,"publicationDate":"2025-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143474045","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Bi-Scale density-plot enhancement based on variance-aware filter

IF 2.5 4区计算机科学 Q2 COMPUTER SCIENCE, SOFTWARE ENGINEERING

Computers & Graphics-Uk

Pub Date : 2025-02-17 DOI: 10.1016/j.cag.2025.104180

Huaiwei Bao , Xin Chen , Kecheng Lu , Chi-Wing Fu , Jean-Daniel Fekete , Yunhai Wang

We present Bi-Scale density Plot (BSP), a new technique to enhance density plots by efficiently optimizing the local density variance in high- and mid-density regions while providing more details in low-density regions. When visualizing large and dense discrete point samples, scatterplots and thematic maps are often employed and we need density plots to further provide aggregated views. However, in the density plots, local patterns such as outliers can be filtered out and meaningful structures such as local density variations can be broken down. The key innovations in BSP include (i) the unified bin–summarize–decompose–combine framework for interactively bi-scale enhancing density plots through combining large- and small-scale density variations; and (ii) the variance-aware filter, which is reformulated based on the edge-preserving image filter, for maintaining the relative data density while reducing the excessive variability in the density plot. Further, BSP can be adopted with a 2D colormap, allowing simultaneous exploration of the enhanced structures and recovering the absolute aggregated densities to improve comparison and lookup tasks. We empirically evaluate our techniques in a controlled study and present two case studies to demonstrate their effectiveness in exploring large data.

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引用次数: 0

3D medical model registration using scale-invariant coherent point drift algorithm for AR

IF 2.5 4区计算机科学 Q2 COMPUTER SCIENCE, SOFTWARE ENGINEERING

Computers & Graphics-Uk

Pub Date : 2025-02-17 DOI: 10.1016/j.cag.2025.104178

Xiaoxing Zhang , Ruifeng Guo , Zhiyong Tong , Hongliang Wang

Registering preoperative 3D medical models to the corresponding regions of the individual in a reality scene is a critical foundation for augmented reality-based (AR-based) surgical navigation systems. A challenge is finding an appropriate spatial mapping function from the medical coordinate system to the AR coordinate system. Our work focuses on registering 3D medical models to the intracranial structures using RGBD point clouds. The mapping function is calculated using local facial features and a scale-invariant coherent point drift (SI-CPD) algorithm that eliminates the scaling parameter. The local facial features significantly reduce mismatched features between the 3D medical models and the RGBD point clouds of the scene, while the proposed SI-CPD algorithm restricts the registration process to translation and rotation operations only. Results demonstrate that our method achieves a target registration error (TRE) of 1.2498 ± 0.0829 mm on private medical datasets and superior registration accuracy on the public Stanford Bunny dataset. Compared to ICP-type methods, the SI-CPD algorithm demonstrates enhanced robustness in handling noise and outliers. Our work introduces a novel methodology to automatically register 3D medical models to the head with high accuracy.

{"title":"3D medical model registration using scale-invariant coherent point drift algorithm for AR","authors":"Xiaoxing Zhang , Ruifeng Guo , Zhiyong Tong , Hongliang Wang","doi":"10.1016/j.cag.2025.104178","DOIUrl":"10.1016/j.cag.2025.104178","url":null,"abstract":"<div><div>Registering preoperative 3D medical models to the corresponding regions of the individual in a reality scene is a critical foundation for augmented reality-based (AR-based) surgical navigation systems. A challenge is finding an appropriate spatial mapping function from the medical coordinate system to the AR coordinate system. Our work focuses on registering 3D medical models to the intracranial structures using RGBD point clouds. The mapping function is calculated using local facial features and a scale-invariant coherent point drift (SI-CPD) algorithm that eliminates the scaling parameter. The local facial features significantly reduce mismatched features between the 3D medical models and the RGBD point clouds of the scene, while the proposed SI-CPD algorithm restricts the registration process to translation and rotation operations only. Results demonstrate that our method achieves a target registration error (TRE) of 1.2498 ± 0.0829 mm on private medical datasets and superior registration accuracy on the public Stanford Bunny dataset. Compared to ICP-type methods, the SI-CPD algorithm demonstrates enhanced robustness in handling noise and outliers. Our work introduces a novel methodology to automatically register 3D medical models to the head with high accuracy.</div></div>","PeriodicalId":50628,"journal":{"name":"Computers & Graphics-Uk","volume":"127 ","pages":"Article 104178"},"PeriodicalIF":2.5,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143453177","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

GEAST-RF: Geometry Enhanced 3D Arbitrary Style Transfer Via Neural Radiance Fields

IF 2.5 4区计算机科学 Q2 COMPUTER SCIENCE, SOFTWARE ENGINEERING

Computers & Graphics-Uk

Pub Date : 2025-02-16 DOI: 10.1016/j.cag.2025.104181

Dong He , Wenhua Qian , Jinde Cao

Style transfer techniques integrated with neural radiance fields enhance the stylization effect of the 3D scene. The objective of 3D style transfer is to render novel views of stylized 3D scenes while maintaining multi-view consistency. However, the current state of 3D style transfer confronts three principal challenges: precise geometric reconstruction, style bias issues, and the artifacts and floaters that frequently emerge during the stylization process. To address these issues, we propose GEAST-RF (Geometry Enhanced 3D Arbitrary Style Transfer Via Neural Radiance Fields), which employs explicit high-level feature grids to represent 3D scenes, achieving detailed geometry reconstruction through volume rendering and high-quality 3D arbitrary style transfer based on target style image information. Specifically, GEAST-RF introduces two pivotal innovations to enhance 3D stylization. The first is the geometry enhancements module, which aligns the geometric structures of stylized views from the same viewpoint to those in the content views, enabling high-precision geometry reconstruction. Thresholding and masking operations are introduced during alignment to alleviate artifacts such as floaters produced during rendering. The second is the adaptive stylization module, which utilizes adaptive computation during the stylization stage to make the model focus more on core style information, reducing reliance on edge style information. Our experiments demonstrate that GEAST-RF can achieve precise geometric structures while providing exceptional 3D stylization effects. A user survey further corroborates these experimental results, revealing that the majority of participants prefer our generated outputs compared to the most recent state-of-the-art methods.

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引用次数: 0

Computing skeleton-based handle/tunnel loops

IF 2.5 4区计算机科学 Q2 COMPUTER SCIENCE, SOFTWARE ENGINEERING

Computers & Graphics-Uk

Pub Date : 2025-02-12 DOI: 10.1016/j.cag.2025.104177

Hayam Abdelrahman, Yiying Tong

Finding surface loops around narrow sections of a surface is widely used as a prepossessing step in various applications such as segmentation, shape analysis, path planning, and robotics. A common approach to locating such loops is based on surface topology. However, such geodesic loops also exist on topologically trivial genus-0 surfaces, where all such loops can continuously deform to a point. While a few existing 3D geometry-aware topological approaches may succeed in detecting such additional narrow loops, their construction can be cumbersome. To extend beyond the limitations of topologically nontrivial independent loops while remaining efficient, we propose a novel approach that leverages the shape’s skeleton for computing surface loops of handle or tunnel type. Given a closed surface mesh, our algorithm produces a practically comprehensive set of loops encircling narrow regions of the volume inside or outside the surface. Notably, our approach streamlines and expedites computations by accepting a skeleton, a 1D representation of the shape, as part of the input. Specifically, handle-type loops are discovered by examining a small subset of the skeleton points as candidate loop centers, while tunnel-type loops are identified by examining only the high-valence skeleton points.

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引用次数: 0

No-reference geometry quality assessment for colorless point clouds via list-wise rank learning

IF 2.5 4区计算机科学 Q2 COMPUTER SCIENCE, SOFTWARE ENGINEERING

Computers & Graphics-Uk

Pub Date : 2025-02-11 DOI: 10.1016/j.cag.2025.104176

Zheng Li , Bingxu Xie , Chao Chu , Weiqing Li , Zhiyong Su

Geometry quality assessment (GQA) of colorless point clouds is crucial for evaluating the performance of emerging point cloud-based solutions (e.g., watermarking, compression, and 3-Dimensional (3D) reconstruction). Unfortunately, existing objective GQA approaches are traditional full-reference metrics, whereas state-of-the-art learning-based point cloud quality assessment (PCQA) methods target both color and geometry distortions, neither of which are qualified for the no-reference GQA task. In addition, the lack of large-scale GQA datasets with subjective scores, which are always imprecise, biased, and inconsistent, also hinders the development of learning-based GQA metrics. Driven by these limitations, this paper proposes a no-reference geometry-only quality assessment approach based on list-wise rank learning, termed LRL-GQA, which comprises of a geometry quality assessment network (GQANet) and a list-wise rank learning network (LRLNet). The proposed LRL-GQA formulates the no-reference GQA as a list-wise rank problem, with the objective of directly optimizing the entire quality ordering. Specifically, a large dataset containing a variety of geometry-only distortions is constructed first, named LRL dataset, in which each sample is label-free but coupled with quality ranking information. Then, the GQANet is designed to capture intrinsic multi-scale patch-wise geometric features in order to predict a quality index for each point cloud. After that, the LRLNet leverages the LRL dataset and a likelihood loss to train the GQANet and ranks the input list of degraded point clouds according to their distortion levels. In addition, the pre-trained GQANet can be fine-tuned further to obtain absolute quality scores. Experimental results demonstrate the superior performance of the proposed no-reference LRL-GQA method compared with existing full-reference GQA metrics. The source code can be found at: https://github.com/VCG-NJUST/LRL-GQA.

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引用次数: 0

A Triple Complementary Stream Network based on forgery feature enhancement and coupling for universal face forgery localization

IF 2.5 4区计算机科学 Q2 COMPUTER SCIENCE, SOFTWARE ENGINEERING

Computers & Graphics-Uk

Pub Date : 2025-02-01 DOI: 10.1016/j.cag.2024.104153

Haoyu Wang , Xu Sun , Yuying Sun , Peihong Li

Existing face forgery detection methods are easily attacked by unknown facial operations and forgery techniques, and cannot accurately locate the forgery area. To solve this problem, we propose a Triple Complementary Stream Network (TCSN) for universal face forgery localization. TCSN innovatively explores universal forgery clues from the depth stream, RGB stream, and frequency stream. First, we construct a feature enhancement module that employs the features of the complementary streams to suppress semantic features and capture the universal forgery features. Subsequently, we design a dynamic affinity graph feature coupling module based on affinity propagation. This module utilizes the correlation between different stream forgery features to promote the transfer of shared and specific features across streams. TCSN achieved state-of-the-art performance on three face forgery localization datasets and demonstrated strong generalization ability. Our code and datasets are available on https://github.com/hywang02/TCSN.

{"title":"A Triple Complementary Stream Network based on forgery feature enhancement and coupling for universal face forgery localization","authors":"Haoyu Wang , Xu Sun , Yuying Sun , Peihong Li","doi":"10.1016/j.cag.2024.104153","DOIUrl":"10.1016/j.cag.2024.104153","url":null,"abstract":"<div><div>Existing face forgery detection methods are easily attacked by unknown facial operations and forgery techniques, and cannot accurately locate the forgery area. To solve this problem, we propose a Triple Complementary Stream Network (TCSN) for universal face forgery localization. TCSN innovatively explores universal forgery clues from the depth stream, RGB stream, and frequency stream. First, we construct a feature enhancement module that employs the features of the complementary streams to suppress semantic features and capture the universal forgery features. Subsequently, we design a dynamic affinity graph feature coupling module based on affinity propagation. This module utilizes the correlation between different stream forgery features to promote the transfer of shared and specific features across streams. TCSN achieved state-of-the-art performance on three face forgery localization datasets and demonstrated strong generalization ability. Our code and datasets are available on <span><span>https://github.com/hywang02/TCSN</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50628,"journal":{"name":"Computers & Graphics-Uk","volume":"126 ","pages":"Article 104153"},"PeriodicalIF":2.5,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143096901","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Visual demonstration of the Hubble law

IF 2.5 4区计算机科学 Q2 COMPUTER SCIENCE, SOFTWARE ENGINEERING

Computers & Graphics-Uk

Pub Date : 2025-02-01 DOI: 10.1016/j.cag.2024.104159

András Fridvalszky, László Szirmay-Kalos

In 1929, Edwin Hubble found observational evidence for that the universe has finite age and is expanding, which provided strong support for the Big Bang theory. The idea is that if galaxies are moving away from each other now, they must have been closer together in the past, eventually leading back to a singular point. Although Hubble’s discovery can be summarized by a very simple equation, the consequences of this phenomenon is hard to imagine without visualization. This paper presents a model for the calculation of the spectral radiance in expanding spaces and a GPU-efficient visualization algorithm to demonstrate the universe expansion. The model allows for the modification of physical parameters, therefore it is appropriate for teaching and testing different scenarios.

引用次数: 0

Three dimensional forest dynamic evolution based on hydraulic erosion and forest fire disturbance

IF 2.5 4区计算机科学 Q2 COMPUTER SCIENCE, SOFTWARE ENGINEERING

Computers & Graphics-Uk

Pub Date : 2025-02-01 DOI: 10.1016/j.cag.2024.104152

Qingkuo Meng, Yongjian Huai, Xiaoying Wang, Ziyang Li, Rui Zhang, Xiaoying Nie

Forest ecosystems can change due to both human activities and climatic factors, particularly shifts in temperature, rain, and wind patterns. Topographic changes caused by rains and structural shifts induced by forest fires represent two primary disturbance events in forest environments. These disturbances are influenced by weather factors and exhibit complex effects on forest dynamics, characterized by regional, seasonal, and stochastic variations. Consequently, examining the interactions between weather patterns and forest evolution through computer graphics holds significant research value. Vegetation and terrain modeling are fundamental to generating realistic forest landscapes. We employ physically-based procedural erosion to simulate geomorphological erosion processes, while further exploring vegetation-terrain interactions to create high-resolution landscapes. Using data from real forest landscapes, we incorporate fire ignition points to simulate forest fire occurrence and spread by modeling wildfire combustion and heat transfer processes, which accurately capture fire dynamics. This enables the simulation of forest fire scenarios under various environmental conditions, allowing us to assess the combined impacts of rainfall and forest fires on forest landscapes. Additionally, the model ensures real-time interaction, supporting the creation of immersive and responsive landscape simulations.

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