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Concept drift challenge in multimedia anomaly detection: A case study with facial datasets 多媒体异常检测中的概念漂移挑战:面部数据集案例研究
IF 3.5 3区 工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2024-01-08 DOI: 10.1016/j.image.2024.117100
Pratibha Kumari , Priyankar Choudhary , Vinit Kujur , Pradeep K. Atrey , Mukesh Saini

Anomaly detection in multimedia datasets is a widely studied area. Yet, the concept drift challenge in data has been ignored or poorly handled by the majority of the anomaly detection frameworks. The state-of-the-art approaches assume that the data distribution at training and deployment time will be the same. However, due to various real-life environmental factors, the data may encounter drift in its distribution or can drift from one class to another in the late future. Thus, a one-time trained model might not perform adequately. In this paper, we systematically investigate the effect of concept drift on various detection models and propose a modified Adaptive Gaussian Mixture Model (AGMM) based framework for anomaly detection in multimedia data. In contrast to the baseline AGMM, the proposed extension of AGMM remembers the past for a longer period in order to handle the drift better. Extensive experimental analysis shows that the proposed model better handles the drift in data as compared with the baseline AGMM. Further, to facilitate research and comparison with the proposed framework, we contribute three multimedia datasets constituting faces as samples. The face samples of individuals correspond to the age difference of more than ten years to incorporate a longer temporal context.

多媒体数据集的异常检测是一个被广泛研究的领域。然而,大多数异常检测框架都忽略了数据中的概念漂移挑战,或者处理不当。最先进的方法假设训练和部署时的数据分布是相同的。然而,由于现实生活中的各种环境因素,数据的分布可能会发生漂移,或者在后期从一个类别漂移到另一个类别。因此,一次性训练的模型可能无法充分发挥作用。在本文中,我们系统地研究了概念漂移对各种检测模型的影响,并提出了一种基于自适应高斯混杂模型(AGMM)的改进框架,用于多媒体数据的异常检测。与基线 AGMM 不同的是,为了更好地处理概念漂移,我们提出的 AGMM 扩展模型将过去的概念记忆更长的时间。广泛的实验分析表明,与基线 AGMM 相比,提议的模型能更好地处理数据漂移。此外,为了便于研究和比较所提出的框架,我们提供了三个以人脸为样本的多媒体数据集。这些人脸样本的年龄相差十多岁,因此具有更长的时间背景。
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引用次数: 0
FAVER: Blind quality prediction of variable frame rate videos FAVER:可变帧频视频的盲质量预测
IF 3.5 3区 工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2024-01-08 DOI: 10.1016/j.image.2024.117101
Qi Zheng , Zhengzhong Tu , Pavan C. Madhusudana , Xiaoyang Zeng , Alan C. Bovik , Yibo Fan

Video quality assessment (VQA) remains an important and challenging problem that affects many applications at the widest scales. Recent advances in mobile devices and cloud computing techniques have made it possible to capture, process, and share high resolution, high frame rate (HFR) videos across the Internet nearly instantaneously. Being able to monitor and control the quality of these streamed videos can enable the delivery of more enjoyable content and perceptually optimized rate control. Accordingly, there is a pressing need to develop VQA models that can be deployed at enormous scales. While some recent effects have been applied to full-reference (FR) analysis of variable frame rate and HFR video quality, the development of no-reference (NR) VQA algorithms targeting frame rate variations has been little studied. Here, we propose a first-of-a-kind blind VQA model for evaluating HFR videos, which we dub the Framerate-Aware Video Evaluator w/o Reference (FAVER). FAVER uses extended models of spatial natural scene statistics that encompass space–time wavelet-decomposed video signals, and leverages the advantages of the deep neural network to provide motion perception, to conduct efficient frame rate sensitive quality prediction. Our extensive experiments on several HFR video quality datasets show that FAVER outperforms other blind VQA algorithms at a reasonable computational cost. To facilitate reproducible research and public evaluation, an implementation of FAVER is being made freely available online: https://github.com/uniqzheng/HFR-BVQA.

视频质量评估(VQA)仍然是一个重要而具有挑战性的问题,它影响着最广泛的许多应用。移动设备和云计算技术的最新进展使得在互联网上捕捉、处理和共享高分辨率、高帧率(HFR)视频几乎成为可能。如果能够监控这些流媒体视频的质量,就能提供更多令人愉悦的内容,并优化感知速率控制。因此,迫切需要开发可大规模部署的 VQA 模型。虽然最近的一些效果已被应用于对可变帧频和高帧频视频质量的全参考(FR)分析,但针对帧频变化的无参考(NR)VQA 算法的开发却鲜有研究。在此,我们首次提出了一种用于评估 HFR 视频的盲 VQA 模型,并将其命名为 "无参考帧率感知视频评估器"(FAVER)。FAVER 使用包含时空小波分解视频信号的空间自然场景统计扩展模型,并利用深度神经网络提供运动感知的优势,进行高效的帧速率敏感质量预测。我们在多个 HFR 视频质量数据集上进行的大量实验表明,FAVER 以合理的计算成本优于其他盲 VQA 算法。为了促进可复制的研究和公共评估,FAVER 的实现可在网上免费获取:https://github.com/uniqzheng/HFR-BVQA。
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引用次数: 0
Stereo vision based systems for sea-state measurement and floating structures monitoring 基于立体视觉的海况测量和浮动结构监测系统
IF 3.5 3区 工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2024-01-06 DOI: 10.1016/j.image.2023.117088
Omar Sallam, Rihui Feng, Jack Stason, Xinguo Wang, Mirjam Fürth

Using computer vision techniques such as stereo vision systems for sea state measurement or for offshore structures monitoring can improve the measurement fidelity and accuracy with no significant additional cost. In this paper, two experiments (in-lab/open-sea) are conducted to study the performance of stereo vision system to measure the water wave surface elevation and rigid body heaving motion. For the in-lab experiment, regular water waves are generated in a wave tank for different frequencies and wave heights, where the water surface is scanned by the stereo vision camera installed on the top of the tank. Surface elevation inferred by the stereo vision is verified by an installed stationary side camera that records the water surface through the tank transparent side window, water surface elevation measured by the side camera recordings is extracted using edge detection algorithm. During the in-lab experiment a heaving buoy is installed to test the performance of Visual Simultaneous Localization and Mapping (VSLAM) algorithm to monitor the buoy heave motion. The VSLAM algorithm fuses a buoy onboard stereo vision recordings with an embedded Inertial Measurement Unit (IMU) to estimate the 6-DOF of a rigid body. The Buoy motion VSLAM measurements are verified by a KLT tracking algorithm implemented on the video recordings of the stationary side camera. The open-sea experiment is implemented in Lake Somerville, Texas. The stereo vision system is installed to measure the water surface elevation and directional spectrum of the wind generated irregular waves. The open-sea wave measurements by the stereo vision are verified by a Sofar commercial wave buoys deployed in the testing location.

使用计算机视觉技术(如立体视觉系统)进行海况测量或近海结构监测,可以在不增加大量成本的情况下提高测量的保真度和准确性。本文进行了两次实验(实验室内/开放海域),研究立体视觉系统测量水波表面高程和刚体翻腾运动的性能。在实验室内实验中,在波浪槽中产生不同频率和波高的规则水波,安装在波浪槽顶部的立体视觉相机对水面进行扫描。立体视觉推断出的水面高程由安装的固定侧置摄像头验证,该摄像头通过水箱透明侧窗记录水面情况,利用边缘检测算法提取侧置摄像头记录测得的水面高程。在实验室内实验中,安装了一个起伏浮标,以测试视觉同步定位和绘图(VSLAM)算法的性能,从而监测浮标的起伏运动。VSLAM 算法将浮标上的立体视觉记录与嵌入式惯性测量单元 (IMU) 相结合,以估计刚体的 6-DOF 运动。浮标运动 VSLAM 测量结果通过在固定侧摄像头视频记录上实施的 KLT 跟踪算法进行验证。公海实验在得克萨斯州萨默维尔湖进行。安装的立体视觉系统用于测量水面高程和风力产生的不规则波浪的方向谱。立体视觉系统测量到的公海波浪由部署在测试地点的 Sofar 商业波浪浮标进行验证。
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引用次数: 0
Enhancing fine-detail image synthesis from text descriptions by text aggregation and connection fusion module 通过文本聚合和连接融合模块,从文本描述中加强精细图像合成
IF 3.5 3区 工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2024-01-02 DOI: 10.1016/j.image.2023.117099
Huaping Zhou , Tao Wu , Senmao Ye , Xinru Qin , Kelei Sun

Synthesizing images with fine details from text descriptions is a challenge. The existing single-stage generative adversarial networks (GANs) fuse sentence features into the image generation process through affine transformation, which alleviate the problems of missing details and large computation from stacked networks. However, existing single-stage networks ignore the word features in the text description, resulting in a lack of detail in the generated image. To address this issue, we proposed a text aggregation module (TAM) to fuse sentence features and word features in a text by a simple spatial attention mechanism. Then we built a text connection fusion (TCF) block consisting mainly of gated recurrent unit (GRU) and up-sampled block. It can connect text features used in the up-sampled blocks to improve text utilization. Besides, to further improve the semantic consistency between text and the generated images, we introduce the deep attentional multimodal similarity model (DAMSM) loss, which monitors the similarity between text and improves semantic consistency. Experimental results prove that our method is superior to the state-of-the-art models on the CUB and COCO datasets, regarding both image fidelity and semantic consistency with the text.

根据文字描述合成具有精细细节的图像是一项挑战。现有的单级生成式对抗网络(GAN)通过仿射变换将句子特征融合到图像生成过程中,从而缓解了堆叠网络所带来的细节缺失和计算量大的问题。然而,现有的单级网络忽略了文本描述中的单词特征,导致生成的图像缺乏细节。为解决这一问题,我们提出了文本聚合模块(TAM),通过简单的空间注意机制融合文本中的句子特征和单词特征。然后,我们建立了一个文本连接融合(TCF)模块,主要由门控递归单元(GRU)和上采样模块组成。它可以连接上采样块中使用的文本特征,提高文本利用率。此外,为了进一步提高文本与生成图像之间的语义一致性,我们引入了深度注意多模态相似性模型(DAMSM)损失,它可以监测文本之间的相似性并提高语义一致性。实验结果证明,在 CUB 和 COCO 数据集上,我们的方法在图像保真度和与文本的语义一致性方面都优于最先进的模型。
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引用次数: 0
Analyzing the effect of shot noise in indirect Time-of-Flight cameras 分析间接飞行时间照相机拍摄噪声的影响
IF 3.5 3区 工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2023-12-29 DOI: 10.1016/j.image.2023.117089
Nofre Sanmartin-Vich , Javier Calpe , Filiberto Pla

Continuous wave indirect Time-of-Flight cameras obtain depth images by emitting a modulated continuous light wave and measuring the delay of the received signal. In this paper we generalize the estimation of the effect of the shot noise when obtaining the phase delay with an arbitrary number of points in the Discrete Fourier Transform, extending and generalizing the analysis done in previous works for the case of four points. For that particular case, we compare our analysis with the state of art. Moreover, we extend the error model using a second order approximation in the error propagation analysis, which provides more accurate estimations according to the Montecarlo simulation experiments. The analysis, based on both analytical and numerical methods, shows that the phase error is, in general, related to the exposure time and weakly to the number of points in the Discrete Fourier Transform. It also depends on the background illumination level, on the amplitude of the received signal, and, when using a three point DFT, on the distance to the objects.

连续波间接飞行时间照相机通过发射调制连续光波并测量接收信号的延迟来获取深度图像。在本文中,我们将离散傅里叶变换中任意点数的相位延迟的估算方法进行了扩展和归纳,并对之前针对四点情况所做的分析进行了概括。针对这种特殊情况,我们将分析结果与最新技术进行了比较。此外,我们还扩展了误差模型,在误差传播分析中使用了二阶近似值,根据蒙特卡洛模拟实验,该近似值提供了更精确的估计。基于分析和数值方法的分析表明,相位误差一般与曝光时间有关,与离散傅里叶变换中的点数关系不大。它还取决于背景光照度、接收信号的振幅,以及使用三点离散傅里叶变换时与物体的距离。
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引用次数: 0
Quantitative analysis of facial soft tissue using weighted cascade regression model applicable for facial plastic surgery 利用适用于面部整形手术的加权级联回归模型对面部软组织进行定量分析
IF 3.5 3区 工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2023-12-05 DOI: 10.1016/j.image.2023.117086
Ali Fahmi Jafargholkhanloo, Mousa Shamsi

Localization of facial landmarks plays an important role in the measurement of facial metrics applicable for beauty analysis and facial plastic surgery. The first step in detecting facial landmarks is to estimate the face bounding box. Clinical images of patients' faces usually show intensity non-uniformity. These conditions cause common face detection algorithms do not perform well in face detection under varying illumination. To solve this problem, a modified fuzzy c-means (MFCM) algorithm is used under varying illumination modeling. The cascade regression method (CRM) has an appropriate performance in face alignment. This algorithm has two main drawbacks. (1) In the training phase, increasing the real data without considering normal data can lead to over-fitting. To solve this problem, a weighted CRM (WCRM) is presented. (2) In the test phase, using a mean shape causes the initial shape to be either near to or far from the face shape. To overcome this problem, a Procrustes-based analysis is presented. One of the most important steps in facial landmark localization is feature extraction. In this study, to increase detection accuracy of the cephalometric landmarks, local phase quantization (LPQ) is used for feature extraction in all three channels of RGB color space. Finally, the proposed algorithm is used to measure facial anthropometric metrics. Experimental results show that the proposed algorithm has a better performance in facial landmark localization than other compared algorithms.

在测量适用于美容分析和面部整形手术的面部指标时,面部地标的定位起着重要作用。检测面部地标的第一步是估计面部边界框。患者面部的临床图像通常显示出强度不均匀性。这些情况导致普通的人脸检测算法在不同光照下的人脸检测效果不佳。为了解决这个问题,在不同光照建模下使用了改进的模糊 c-means 算法(MFCM)。级联回归法(CRM)在人脸配准方面具有适当的性能。该算法有两个主要缺点。(1) 在训练阶段,增加真实数据而不考虑正常数据会导致过度拟合。为了解决这个问题,提出了一种加权 CRM(WCRM)。(2) 在测试阶段,使用平均形状会导致初始形状接近或远离脸部形状。为了克服这一问题,提出了一种基于 Procrustes 的分析方法。面部地标定位最重要的步骤之一是特征提取。在本研究中,为了提高头颅测量地标的检测准确性,在 RGB 色彩空间的所有三个通道中都使用了局部相位量化(LPQ)进行特征提取。最后,提出的算法被用于测量面部人体测量指标。实验结果表明,与其他同类算法相比,所提出的算法在面部地标定位方面具有更好的性能。
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引用次数: 0
DSRNet: Depth Super-Resolution Network guided by blurry depth and clear intensity edges DSRNet:由模糊深度和清晰强度边缘引导的深度超分辨率网络
IF 3.5 3区 工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2023-11-28 DOI: 10.1016/j.image.2023.117064
Hui Lan, Cheolkon Jung

Although high resolution (HR) depth images are required in many applications such as virtual reality and autonomous navigation, their resolution and quality generated by consumer depth cameras fall short of the requirements. Existing depth upsampling methods focus on extracting multiscale features of HR color image to guide low resolution (LR) depth upsampling, thus causing blurry and inaccurate edges in depth. In this paper, we propose a depth super-resolution (SR) network guided by blurry depth and clear intensity edges, called DSRNet. DSRNet differentiates effective edges from a number of HR edges with the guidance of blurry depth and clear intensity edges. First, we perform global residual estimation based on an encoder–decoder architecture to extract edge structure from HR color image for depth SR. Then, we distinguish effective edges from HR edges in the decoder side with the guidance of LR depth upsampling. To maintain edges for depth SR, we use intensity edge guidance that extracts clear intensity edges from HR image. Finally, we use residual loss to generate accurate high frequency (HF) residual and reconstruct HR depth maps. Experimental results show that DSRNet successfully reconstructs depth edges in SR results as well as outperforms the state-of-the-art methods in terms of visual quality and quantitative measurements.1

尽管虚拟现实和自主导航等许多应用都需要高分辨率(HR)深度图像,但消费级深度相机生成的深度图像的分辨率和质量却达不到要求。现有的深度升采样方法主要是提取高分辨率彩色图像的多尺度特征来指导低分辨率(LR)深度升采样,因此会造成深度边缘模糊和不准确。在本文中,我们提出了一种由模糊深度和清晰强度边缘引导的深度超分辨率(SR)网络,称为 DSRNet。DSRNet 在模糊深度和清晰强度边缘的引导下,从大量 HR 边缘中区分出有效边缘。首先,我们基于编码器-解码器架构进行全局残差估计,从高清彩色图像中提取深度 SR 的边缘结构。然后,在解码器侧,我们以 LR 深度上采样为指导,将有效边缘与 HR 边缘区分开来。为了保持深度 SR 的边缘,我们使用强度边缘引导,从 HR 图像中提取清晰的强度边缘。最后,我们使用残差损耗来生成精确的高频(HF)残差,并重建 HR 深度图。实验结果表明,DSRNet 成功地重建了 SR 结果中的深度边缘,并在视觉质量和定量测量方面优于最先进的方法。
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引用次数: 0
A dual fusion deep convolutional network for blind universal image denoising 用于盲通用图像去噪的双融合深度卷积网络
IF 3.5 3区 工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2023-11-12 DOI: 10.1016/j.image.2023.117077
Zhiyu Lyu, Yan Chen, Haojun Sun, Yimin Hou

Blind image denoising and edge-preserving are two primary challenges to recover an image from low-level vision to high-level vision. Blind denoising requires a single denoiser can denoise images with any intensity of noise, and it has practical utility since accurate noise levels cannot be acquired from realistic images. On the other hand, edge preservation can provide more image features for subsequent processing which is also important for the denoising. In this paper, we propose a novel blind universal image denoiser to remove synthesis and realistic noise while preserving the image texture. The denoiser consists of noise network and prior network parallelly, and then a fusion block is used to give the weight between these two networks to balance computation cost and denoising performance. We also use the Non-subsampled Shearlet Transform (NSST) to enlarge the size of receptive field to obtain more detailed information. Extensive denoising experiments on synthetic images and realistic images show the effectiveness of our denoiser.

盲图像去噪和边缘保持是将图像从低级视觉恢复到高级视觉的两个主要问题。盲去噪要求单个去噪器可以对任意噪声强度的图像进行去噪,由于无法从真实图像中获得准确的噪声水平,因此具有实用价值。另一方面,边缘保留可以为后续处理提供更多的图像特征,这对去噪也很重要。本文提出了一种新的盲通用图像去噪方法,在保持图像纹理的同时去除合成噪声和真实噪声。该去噪器由噪声网络和先验网络并行组成,然后使用融合块在两个网络之间分配权重,以平衡计算成本和去噪性能。我们还使用非下采样Shearlet变换(NSST)来扩大接收野的大小,以获得更详细的信息。对合成图像和真实图像的去噪实验表明了该去噪方法的有效性。
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引用次数: 0
ClGanNet: A novel method for maize leaf disease identification using ClGan and deep CNN ClGanNet:基于ClGan和深度CNN的玉米叶片病害识别新方法
IF 3.5 3区 工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2023-11-04 DOI: 10.1016/j.image.2023.117074
Vivek Sharma , Ashish Kumar Tripathi , Purva Daga , Nidhi M. , Himanshu Mittal

With the advancement of technologies, automatic plant leaf disease detection has received considerable attention from researchers working in the area of precision agriculture. A number of deep learning-based methods have been introduced in the literature for automated plant disease detection. However, the majority of datasets collected from real fields have blurred background information, data imbalances, less generalization, and tiny lesion features, which may lead to over-fitting of the model. Moreover, the increased parameter size of deep learning models is also a concern, especially for agricultural applications due to limited resources. In this paper, a novel ClGan (Crop Leaf Gan) with improved loss function has been developed with a reduced number of parameters as compared to the existing state-of-the-art methods. The generator and discriminator of the developed ClGan have been encompassed with an encoder–decoder network to avoid the vanishing gradient problem, training instability, and non-convergence failure while preserving complex intricacies during synthetic image generation with significant lesion differentiation. The proposed improved loss function introduces a dynamic correction factor that stabilizes learning while perpetuating effective weight optimization. In addition, a novel plant leaf classification method ClGanNet, has been introduced to classify plant diseases efficiently. The efficiency of the proposed ClGan was validated on the maize leaf dataset in terms of the number of parameters and FID score, and the results are compared against five other state-of-the-art GAN models namely, DC-GAN, W-GAN, WGanGP, InfoGan, and LeafGan. Moreover, the performance of the proposed classifier, ClGanNet, was evaluated with seven state-of-the-art methods against eight parameters on the original, basic augmented, and ClGan augmented datasets. Experimental results of ClGanNet have outperformed all the considered methods with 99.97% training and 99.04% testing accuracy while using the least number of parameters.

随着技术的进步,植物叶片病害自动检测受到了精准农业研究人员的广泛关注。文献中介绍了许多基于深度学习的植物病害自动检测方法。然而,大多数从真实领域采集的数据集存在背景信息模糊、数据不平衡、泛化程度低、病灶特征微小等问题,可能导致模型的过拟合。此外,由于资源有限,深度学习模型的参数大小增加也是一个问题,特别是在农业应用中。在本文中,与现有的最先进的方法相比,开发了一种具有改进损失函数的新型ClGan(作物叶片Gan),其参数数量减少。开发的ClGan的生成器和鉴别器包含了一个编码器-解码器网络,以避免梯度消失问题、训练不稳定和非收敛失败,同时保留了合成图像生成过程中复杂的复杂性。所提出的改进损失函数引入了一个动态校正因子,在保持有效权优化的同时稳定学习。此外,还引入了一种新的植物叶片分类方法ClGanNet,对植物病害进行有效分类。根据参数数量和FID评分,在玉米叶片数据集上验证了所提出的ClGan的效率,并将结果与其他五种最先进的GAN模型(DC-GAN、W-GAN、WGanGP、InfoGan和LeafGan)进行了比较。此外,在原始数据集、基本增强数据集和ClGan增强数据集上,用7种最先进的方法对8个参数进行了性能评估。实验结果表明,ClGanNet在使用最少参数的情况下,以99.97%的训练精度和99.04%的测试精度优于所有考虑的方法。
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引用次数: 0
Image tone mapping based on clustering and human visual system models 基于聚类和人类视觉系统模型的图像色调映射
IF 3.5 3区 工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2023-10-26 DOI: 10.1016/j.image.2023.117075
Xueyu Han , Ishtiaq Rasool Khan , Susanto Rahardja

Natural scenes generally have very high dynamic range (HDR) which cannot be captured in the standard dynamic range (SDR) images. HDR imaging techniques can be used to capture these details in both dark and bright regions, and the resultant HDR images can be tone mapped to reproduce them on SDR displays. To adapt to different applications, the tone mapping operator (TMO) should be able to achieve high performance for diverse HDR scenes. In this paper, we present a clustering-based TMO by embedding human visual system models that function effectively in different scenes. A hierarchical scheme is applied for clustering to reduce the computational complexity. We also propose a detail preservation method by superimposing the details of original HDR images to enhance local contrasts, and a color preservation method by limiting the adaptive saturation parameter to control the color saturation attenuating. The effectiveness of our method is assessed by comparing with state-of-the-art TMOs quantitatively on large-scale HDR datasets and qualitatively with a group of subjects. Experimental results of both objective and subjective evaluations show that the proposed method achieves improvements over the competing methods in generating high quality tone-mapped images with good contrast and natural color appearance for diverse HDR scenes.

自然场景通常具有非常高的动态范围(HDR),这是标准动态范围(SDR)图像无法捕捉到的。HDR成像技术可用于在暗区和亮区捕获这些细节,并且由此产生的HDR图像可以色调映射以在SDR显示器上再现它们。为了适应不同的应用,色调映射算子(TMO)应该能够在不同的HDR场景中实现高性能。在本文中,我们通过嵌入在不同场景中有效工作的人类视觉系统模型,提出了一种基于聚类的TMO。为了降低计算复杂度,采用了一种分层的聚类方法。我们还提出了一种通过叠加原始HDR图像的细节来增强局部对比度的细节保存方法,以及一种通过限制自适应饱和度参数来控制色彩饱和度衰减的颜色保存方法。通过在大规模HDR数据集上与最先进的TMOs进行定量比较,并与一组受试者进行定性比较,评估了我们方法的有效性。客观和主观评价的实验结果表明,该方法在生成高质量、对比度好、色彩自然的HDR场景色调映射图像方面取得了一定的进步。
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引用次数: 0
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Signal Processing-Image Communication
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