An edge-weighted second order variational model for image decomposition

Decomposing an image into structure and texture is an important procedure for image understanding and analysis. Structure retains object hues and sharp edges whilst texture contains oscillating patterns of an observed image. The classical Vese–Osher model has been used for image decomposition, but its resulting structure image tends to show the undesirable staircase effect. Second order variational models that use a bounded Hessian regulariser have been proposed to remedy this side effect, but they tend to blur edges of objects in structure components. In this paper, we propose an edge-weighted second order variational model for image decomposition, which is able to eliminate staircase effects and preserve object edges. To avoid directly calculating the high order nonlinear partial differential equations of the proposed model, a fast split Bregman algorithm is developed, which uses the fast Fourier transform and analytical generalised soft thresholding equations. Extensive experiments demonstrate that the proposed variational image decomposition model outperforms state-of-the-art first and second order image decomposition models. By removing the texture component from the original noisy image, the effectiveness of the proposed model for image denoising has also been validated.     

基于GAN先验的图像补全方法

设计和利用良好的图像先验知识是解决图像补全问题的重要方式.生成对抗网络(GAN)作为一种优秀的生成式模型,其生成器可以从大型图像数据集中学习到丰富的图像语义信息,将预训练GAN模型作为图像先验是一种好的选择.为了利用预训练GAN模型更好地解决图像补全问题,本文在使用多个隐变量的基础上,在预训练生成器中间层同时对通道和特征图添加自适应权重,并在训练过程中微调生成器参数.最后通过图像重建和图像补全实验,定性和定量分析相结合,证实了本文提出的方法可以有效地挖掘预训练模型的先验知识,进而高质量地完成图像补全任务.     

Bimode model for face recognition and face representation

Tensorface based approaches decompose an image into its constituent factors (i.e., person, lighting, viewpoint, etc.), and then utilize these factor spaces for recognition. However, tensorface is not a preferable choice, because of the complexity of its multimode. In addition, a single mode space, except the person-space, could not be used for recognition directly. From the viewpoint of practical application, we propose a bimode model for face recognition and face representation. This new model can be treated as a simplified model representation of tensorface. However, their respective algorithms for training are completely different, due to their different definitions of subspaces. Thanks to its simpler model form, the proposed model requires less iteration times in the process of training and testing. Moreover bimode model can be further applied to an image reconstruction and image synthesis via an example image. Comprehensive experiments on three face image databases (PEAL, YaleB frontal and Weizmann) validate the effectiveness of the proposed new model.     

Model-based location of automated guided vehicles in the navigation sessions by 3d computer vision

A model-based approach to determining the location of an automated guided vehicle (AGV) in the navigation session is proposed. Significant scenes along the navigation path are taken and stored as model images. Also, specific templates of each model image are chosen in advance for matching. When the AGV approaches one significant scene in the navigation session, an input image is taken and the corresponding model image is fetched. Each template in the model image is matched with the input image through the use of the three-step method. The templates are directly used for matching; therefore, no special image processing techniques are required for the input image in the navigation session. In addition, several templates in a model image are used for matching instead of the model image itself. This promotes the robustness of the matching results and saves the computation cost. Furthermore, the 2D string method is used as a post-verifier to discard erroneous matching results. The remaining correct matching results then can be used to determine the vehicle location on the navigation path. Simulated experiments are implemented and the results confirm the feasibility of the proposed approach. © 1994 John Wiley & Sons, Inc.     

基于块运动矢量加权的Snakes模型及在序列图像分割中的应用

经典的Snakes模型具有开放的、统一的架构，在此基础上，为了分割复杂背景的序列图像，产生了各种改进的Snakes模型，但都存在着不足：计算量大、需要先验知识、易受光流计算精度影响等。针对这些缺点，提出了块运动矢量加权的Snakes模型，可以用于复杂背景序列图像的分割。这种模型以图像中的边缘信息为分割的最终依据，结合块运动估计的结果，增强了序列图像分割的鲁棒性。根据运动场估计的结果在该模型中所起的作用，提出了边缘优先的块运动估计算法，大大减少了计算量。用块运动矢量加权的Snakes模型分割复杂背景序列图像，取得了好的分割结果。     

逆滤波器技术复原均匀散焦图像的探讨

在图像退化模型中，均匀散焦模糊是一种常见模型。文中提出了一种判断均匀散焦模糊的方法，而逆滤波器法是复原均匀散焦的常用算法，对于不同的散焦半径，用逆滤波器法恢复后会得到不同的复原图，据此作者设计了一种判断图像清晰度的方法。实验证明以上算法和步骤是有效的，具有一定的适用性。     

Political Optimizer with Deep Learning-Enabled Tongue Color Image Analysis Model

Biomedical image processing is widely utilized for disease detection and classification of biomedical images. Tongue color image analysis is an effective and non-invasive tool for carrying out secondary detection at anytime and anywhere. For removing the qualitative aspect, tongue images are quantitatively inspected, proposing a novel disease classification model in an automated way is preferable. This article introduces a novel political optimizer with deep learning enabled tongue color image analysis (PODL-TCIA) technique. The presented PODL-TCIA model purposes to detect the occurrence of the disease by examining the color of the tongue. To attain this, the PODL-TCIA model initially performs image pre-processing to enhance medical image quality. Followed by, Inception with ResNet-v2 model is employed for feature extraction. Besides, political optimizer (PO) with twin support vector machine (TSVM) model is exploited for image classification process, shows the novelty of the work. The design of PO algorithm assists in the optimal parameter selection of the TSVM model. For ensuring the enhanced outcomes of the PODL-TCIA model, a wide-ranging experimental analysis was applied and the outcomes reported the betterment of the PODL-TCIA model over the recent approaches.     

基于同态滤波的水下图像增强与色彩校正模型

水体对于不同波长的光信号衰减程度不一致,这种现象破坏了水下图像的清晰度和色彩恒定性。为了解决水下图像亮度与色彩扭曲问题,提出一种基于同态滤波的水下图像增强与色彩校正模型。首先,通过比尔-朗伯定律和路径辐射分量构建出水下成像模型。其次,通过同态滤波对未经过衰减的水下图像进行估计。最后,通过麦克劳林级数对水下成像模型进行级数展开,进而推导出一种保持颜色恒定的水下图像色彩校正模型。实验部分分别对比了水下图像的主观视觉效果和客观评价指标,验证了该算法能够有效地保证水下图像的清晰度和色彩恒定性。校正后的水下图像细节丰富,色彩逼真。     

基于图像的光照模型研究综述

从传统图形学的绘制技术与基于图像的绘制技术相结合的角度出发,以全光函数这个基于图像的绘制技术的理论基础为核心,概括性地提出基于图像的光照研究的基本任务实际上是对全光函数的采样、重建、合成和重采样的过程,并进一步地指出,基于图像的光照研究的重要意义在于扩展了原有基于图像的绘制技术中只能改变视点位置和视线方向的限制,使之可以通过改变场景本身的组成成分产生出更加丰富的光照效果。同时,该文综述性地分析了近期内有关基于图像的光照问题的部分研究工作,并从如何改变场景光照条件的角度出发,按照所使用的光照模型的不同,将这些方法分成三大类,即利用传统光照模型的方法、利用基于图像的光照模型的方法以及无需光照模型的方法。并从这个分类框架出发,进一步分析指出,利用基于图像的光照模型的方法将是未来研究的重点,并沿着这一方向尝试性地提出了一种新的模型。     

深度学习图像描述方法分析与展望

图像描述任务是利用计算机自动为已知图像生成一个完整、通顺、适用于对应场景的描述语句，实现从图像到文本的跨模态转换。随着深度学习技术的广泛应用，图像描述算法的精确度和推理速度都得到了极大提升。本文在广泛文献调研的基础上，将基于深度学习的图像描述算法研究分为两个层面，一是图像描述的基本能力构建，二是图像描述的应用有效性研究。这两个层面又可以细分为传递更加丰富的特征信息、解决暴露偏差问题、生成多样性的图像描述、实现图像描述的可控性和提升图像描述推理速度等核心技术挑战。针对上述层面所对应的挑战，本文从注意力机制、预训练模型和多模态模型的角度分析了传递更加丰富的特征信息的方法，从强化学习、非自回归模型和课程学习与计划采样的角度分析了解决暴露偏差问题的方法，从图卷积神经网络、生成对抗网络和数据增强的角度分析了生成多样性的图像描述的方法，从内容控制和风格控制的角度分析了图像描述可控性的方法，从非自回归模型、基于网格的视觉特征和基于卷积神经网络解码器的角度分析了提升图像描述推理速度的方法。此外，本文还对图像描述领域的通用数据集、评价指标和已有算法性能进行了详细介绍，并对图像描述中待解决的问题与未来研究...     

JPEG2000图像无线传输统计性能分析与误码保护研究

JPEG2000标准是最新的图像压缩标准．首先分析了JPEG2000标准中的抗误码方法，进而提出了一个分析模型来预测在无线信道上传输的JPEG2000编码图像重建质量，并通过仿真加以验证．此分析模型可用来在无线基站上为JPEG2000编码图像的传输设计高效的非均匀误码保护方案．基于该分析模型，定义了一个效用函数来在图像重建质量和传输开销之间进行折中。以决定采用何种非均匀误码保护方案。达到最佳的传输质量．     

采用多通道浅层CNN构建的多降噪器最优组合模型

现有的一致性神经网络(Consensus neural network, CsNet)利用凸优化和神经网络技术将多个降噪算法(降噪器)输出的图像进行加权组合(融合), 以获得更好的降噪效果, 但该优化模型在降噪效果和执行效率方面仍有较大改进空间. 为此, 提出一种基于轻量型多通道浅层卷积神经网络(Multi-channel shallow convolutional neural network, MSCNN)构建的多降噪器最优组合(Optimal combination of image denoisers, OCID)模型. 该模型采用多通道输入结构直接接收由多个降噪器输出的降噪图像, 并利用残差学习技术合并完成图像融合和图像质量提升两项任务. 具体使用时, 对于给定的一张噪声图像, 先用多个降噪器对其降噪, 并将降噪后图像输入OCID模型获得残差图像, 然后将多个降噪图像的均值图像与残差图像相减, 所得到图像作为优化组合后的降噪图像. 实验结果表明, 与CsNet组合模型相比, 网络结构更为简单的OCID模型以更小的计算代价获得了图像质量更高的降噪图像.     

Effective image annotation for searches using multilevel semantics

There is an increasing need for automatic image annotation tools to enable effective image searching in digital libraries. In this paper, we present a novel probabilistic model for image annotation based on content-based image retrieval techniques and statistical analysis. One key difficulty in applying statistical methods to the annotation of images is that the number of manually labeled images used to train the methods is normally insufficient. Numerous keywords cannot be correctly assigned to appropriate images due to lacking or missing information in the labeled image databases. To deal with this challenging problem, we also propose an enhanced model in which the annotated keywords of a new image are defined in terms of their similarity at different semantic levels, including the image level, keyword level, and concept level. To avoid missing some relevant keywords, the model labels the keywords with the same concepts as the new image. Our experimental results show that the proposed models are effective for annotating images that have different qualities of training data.     

基于特征轮廓的灰度图像定位三维物体方法

讨论了一种基于特征轮廓的从三维灰度图像确定三维物体位置和姿态的方法，该方法首先建立物体的三维网页模型，检测模型上的特征点，并建立该物体的特征轮廓模型，然后检测输入图像中物体上的特征点，形成特征轮廓，并与特征轮廓模型相匹配，就可得到该物体在三维空间中的姿态；最后使用最小二乘法对物体进行精确定位，实验证明，该方法在物体遮挡情况下不是很严重时，可以快速精确地从灰度图像定位三维物体。     

MDS-based segmentation model for the fusion of contour and texture cues in natural images

In this paper, we present an original image segmentation model based on a preliminary spatially adaptive non-linear data dimensionality reduction step integrating contour and texture cues. This new dimensionality reduction model aims at converting an input texture image into a noisy color image in order to greatly simplify its subsequent segmentation. In this latter de-texturing model, the (spatially adaptive) non-local constraints based on edge and contour cues allows us to efficiently regularize the reduced data (or the resulting de-textured color image) and to efficiently combine inhomogeneous region and edge based features in a data fusion/reduction model used as pre-processing step for a final segmentation task. In addition, a set of color/texture and edge-based adaptive spatial continuity constraints is imposed during the segmentation step. These improvements lead to an appealing and powerful two-step adaptive segmentation model, integrating contour and texture cues. Extensive experimental evaluation on the Berkeley image segmentation database demonstrates the efficiency of this hybrid segmentation model in terms of classification accuracy of pairwise pixels in the resulting segmentation map and in the precision–recall framework widespread used for evaluating contour detectors.     

图注意力网络的场景图到图像生成模型

目的 目前文本到图像的生成模型仅在具有单个对象的图像数据集上表现良好，当一幅图像涉及多个对象和关系时，生成的图像就会变得混乱。已有的解决方案是将文本描述转换为更能表示图像中场景关系的场景图结构，然后利用场景图生成图像，但是现有的场景图到图像的生成模型最终生成的图像不够清晰，对象细节不足。为此，提出一种基于图注意力网络的场景图到图像的生成模型，生成更高质量的图像。方法 模型由提取场景图特征的图注意力网络、合成场景布局的对象布局网络、将场景布局转换为生成图像的级联细化网络以及提高生成图像质量的鉴别器网络组成。图注意力网络将得到的具有更强表达能力的输出对象特征向量传递给改进的对象布局网络，合成更接近真实标签的场景布局。同时，提出使用特征匹配的方式计算图像损失，使得最终生成图像与真实图像在语义上更加相似。结果 通过在包含多个对象的COCO-Stuff图像数据集中训练模型生成64×64像素的图像，本文模型可以生成包含多个对象和关系的复杂场景图像，且生成图像的Inception Score为7.8左右，与原有的场景图到图像生成模型相比提高了0.5。结论 本文提出的基于图注意力网络的场景图到图像生成模型不仅可以生成包含多个对象和关系的复杂场景图像，而且生成图像质量更高，细节更清晰。     

各向异性扩散与高斯差分模型的多聚焦图像融合方法

利用各向异性扩散模型具有良好的边缘保持特性,提出一种基于各向异性扩散滤波与高斯滤波差分规则的图像融合算法。各向异性扩散方程对图像进行滤波操作,在图像的同质区域实施正向扩散以平滑图像,而在图像边缘实行较弱平滑以保护边缘细节信息。将通过各向异性扩散模型处理的图像与经过高斯函数滤波的结果图像进行差分操作,可以得到图像的高频系数信息。为提高健壮性,对高频系数进行小窗口累加,其作为像素选择准则,再分别从原始图像中直接获取对应的像素值组成融合结果图像。实验结果表明,所提出的方法可以有效地融合源图像信息,非常适合多聚焦     

Non Sub-Sampled Contourlet with Joint Sparse Representation Based Medical Image Fusion

Medical Image Fusion is the synthesizing technology for fusing multimodal medical information using mathematical procedures to generate better visual on the image content and high-quality image output. Medical image fusion represents an indispensible role in fixing major solutions for the complicated medical predicaments, while the recent research results have an enhanced affinity towards the preservation of medical image details, leaving color distortion and halo artifacts to remain unaddressed. This paper proposes a novel method of fusing Computer Tomography (CT) and Magnetic Resonance Imaging (MRI) using a hybrid model of Non Sub-sampled Contourlet Transform (NSCT) and Joint Sparse Representation (JSR). This model gratifies the need for precise integration of medical images of different modalities, which is an essential requirement in the diagnosing process towards clinical activities and treating the patients accordingly. In the proposed model, the medical image is decomposed using NSCT which is an efficient shift variant decomposition transformation method. JSR is exercised to extricate the common features of the medical image for the fusion process. The performance analysis of the proposed system proves that the proposed image fusion technique for medical image fusion is more efficient, provides better results, and a high level of distinctness by integrating the advantages of complementary images. The comparative analysis proves that the proposed technique exhibits better-quality than the existing medical image fusion practices.     

采用增量型非负矩阵分解建模的目标跟踪算法

建立鲁棒的外观模型是目标跟踪中的关键问题,为此提出一种基于增量型非负矩阵分解的目标跟踪算法.首先根据转移概率模型在当前帧中预测得到一组图像样本;随后利用非负矩阵分解获取样本在子空间中的坐标向量;在此基础上计算样本与前一帧视频中目标图像在低维坐标向量上的相关性,以具有最大相关性的图像样本作为目标在当前帧中的图像区域;最后以增量的方式完成子空间的在线更新,提高了外观模型的更新效率,且所要求的存储空间大小恒定.实验结果表明,该算法对目标物的外观变化具有良好的自适应性,能够在视频序列中对目标进行稳定的跟踪.