基于压缩感知的图像处理与重构方法

针对图像修复和插值等图像处理问题进行研究,通过将图像修复和插值模型在压缩感知(CS)理论的框架下进行转换,建立了新的图像修复和插值模型,该模型与CS理论中的重构模型相对应。对此转化得到的重构问题,基于图像在复数小波变换上的稀疏性,利用迭代硬阈值方法求解重构模型,进而获得重构图像。仿真和实测数据处理结果验证本文方法的有效性。     

基于Curvelet方向特征的样本块图像修复算法

能否保持修复后图像的结构连贯性和邻域一致性决定了修复性能的优劣.为提高现有样本块修复算法性能,本文提出基于Curvelet变换的样本块图像修复算法.首先利用Curvelet变换估计待修复图像的4方向特征.然后利用颜色信息与方向信息共同衡量样本块间的相似度,在此基础上构造颜色-方向结构稀疏度函数.同时根据构造的加权颜色-方向距离寻找合适的多个匹配块,并利用多个匹配块在构造的颜色和方向空间内的邻域一致性约束下稀疏表示目标块,同时根据目标块所处区域特性自适应确定误差容限.实验结果表明提出算法较现有算法可获得更优的修复效果,尤其是在修复富含结构纹理破损类型的图像时.     

基于距离辅助的超宽带MIMO雷达图像人体姿态重构网络

超宽带多输入多输出（Multiple-input Multiple-output, MIMO）雷达可以获取目标的多维信息,在目标探测和人体动作分类等方面有很大的优势。然而,在实际应用中,超宽带MIMO雷达获取的人体目标成像结果通常分辨率较低,抽象难懂,且目标距离越远雷达图像分辨率越低。针对以上问题,本文提出了一种基于距离辅助的超宽带MIMO雷达图像人体姿态重构网络,首先使用卷积神经网络提取人体目标成像的信号强度和空间位置特征,然后使用反卷积模块重构出人体目标的各个关节点位置。同时,考虑雷达成像结果随着距离的变远而恶化,本文将目标的距离作为辅助信息来选择合适的网络模型参数,进而提高姿态重构的精度。实验结果表明,本方法可以将抽象的人体目标雷达图像转化为易于理解的人体关节姿态,且有较好的姿态重构性能,极大增强了传统雷达图像的可视化性能。同时,距离信息的引入提高了姿态重构精度,有效克服了距离增大带来的影响。      

空间差异度量约束的图像修复

针对传统算法在图像修复时选取最佳块匹配准则的单一性和随机性的问题,提出了一种空间差异度量约束的图像修复方法。该算法利用等照度线确定待修复块的优先级,根据空间差异度量约束的最佳块匹配准则搜索与该待修复块最匹配的样本块,采用最佳匹配块的信息更新优先级最高的待修复块的信息以修复其缺失像素信息。实验结果表明,与传统算法相比,所提算法能有效提高图像修复的主客观质量,对三种类型的信息缺失均取得了较好的修复效果。     

分层级联生成对抗网络用于手背静脉图像修复

为解决在识别过程中因手背静脉图像信息缺失而造成识别效率低下的问题,本文提出了分层级联生成对抗网络的手背静脉图像修复框架。该网络框架分别以级联与并行分层的方式进行修复操作,通过并行分层结构创新性的融合了不同静脉图像的特征信息;为有效地利用静脉图像的上下文信息对缺失的静脉图像信息进行预测与补全,在网络中创新性的引入了空洞卷积核与非局部注意力网络;为保证修复静脉图像质量与其真实图像的一致性,创新性的结合对抗损失与感知损失进行优化。实验结果表明,本文算法在视觉效果、峰值信噪比（Peak Signal to Noise Ratio,PSNR）和结构相似性（Structural Similarity Index,SSIM）等方面表现优于已有算法,并在两个公开的掌纹与指纹数据集上进行了有效的泛化验证。此外,修复图像相较于缺失图像在身份识别效率方面有了一定的提高。      

基于样本和线性结构信息的大范围图像修复算法

基于纹理的图像修复算法对于修复破损区域比较大的图像效果较好,但该算法对于含有结构信息的图像修复效果很差.通过新的优先项的计算、平均值补偿及增加惩罚项提高传统的基于样本的图像修复算法的修复效果,结合图像中常出现的直线和曲线结构特征,提出了基于样本和结构信息的大范围图像修复算法.实验表明,该算法易于实现,修复结果能达到令人满意的效果,具有较高的实用价值.     

采用图像修复的基于深度图像复制

在传统的基于深度图像复制(DIBR)的基础上提出一种基于图像修复的DIBR方法,将预处理深度图像和图像修复算法相结合来填补三维图像映射后的空洞。与传统方法相比更加灵活,本文方法仅需传输一路参考图像序列,从而有效降低DIBR系统的传输带宽。实验结果证明,本文所提出方法是有效的。     

基于分形的图像修复算法

 图像修复是目前图像处理领域中的一个研究热点,对于较大孔洞的修复一直是个难点问题,已有算法都未能很好地解决.本文基于分形相关理论,提出了一种新的修复算法,很好地利用了图像的整体信息.论述了分形维数和分形编码序列块大小之间的关系,提出多尺度的分形编码及重构的修复方法.为了强化图像细节信息,进行了分形局部迭代.为了提高图像修复的质量,将图像进行了分形放大,再进行分形插值修复.从实验结果可以看出,新方法取得了较好的修补效果,尤其是对纹理图像和有较大孔洞的图像效果更好.     

基于深度学习的数字图像修复算法最新进展

数字图像修复是一项利用计算机技术还原破损图像的缺失信息,从而实现自动修复破损图像的技术,其广泛应用于文物修复、图像去雾、电影特效生成等方面。近年来深度学习的发展为图像修复提供了新的思路,即将估计缺失信息的问题转为有条件的图像生成问题。基于深度学习的图像修复研究已成为底层计算机视觉问题的研究热点之一。本文对深度学习在数字图像修复领域的最新进展进行总结归纳,并详细阐述卷积模式和网络结构优化的研究进展,最后对未来的研究方向进行展望。      

一种改进的BSCB图像修补算法

针对传统BSCB算法对颜色复杂度高或缺损区域较大的图像修补效果较差的问题,提出一种改进的BSCB图像修补算法.考虑传统算法中初始化、光滑算子和修补扩散等步骤中存在的缺陷,分别对其进行了改进.改进的BSCB算法进行图像修补时,采用随机初始化方法,引入平滑和梯度算子代替原拉普拉斯图像平滑算子,并采用加权平均算法选择所有邻点进行异向性扩散,从而得到最终修补结果.实验表明,新方法修补图像,特别是修补颜色复杂度高、缺损区域较大的图像具有较好的效果.     

Image segmentation and inpainting using hierarchical level set and texture mapping

Image inpainting is an artistic procedure to recover a damaged painting or picture. We propose a novel approach for image inpainting by using the Mumford-Shah (MS) model and the level set method to estimate image structure of the damaged regions. This approach has been successfully used in image segmentation problem. Compared to some other inpainting methods, the MS model approach detects and preserves edges in the inpainting areas. We propose a fast and efficient algorithm that achieves both inpainting and segmentation. In previous works on the MS model, only one or two level set functions are used to segment an image. While this approach works well on simple cases, detailed edges cannot be detected in complicated image structures. Although multi-level set functions can be used to segment an image into many regions, the traditional approach causes extensive computations and the solutions depend on the location of initial curves. Our proposed approach utilizes faster hierarchical level set method and guarantees convergence independent of initial conditions. Because we detect both the main structure and the detailed edges, our approach preserves edges in the inpainting area. Also, exemplar-based approach for filling textured regions is employed. Experimental results demonstrate the advantage of our method.     

基于MSFM的快速图像修补算法

近年来,基于非线性高阶偏微分方程的高质量图像修补算法已经得到了发展,但这些方法需要大量的迭代,时间开销大,复杂度高.Telea提出的基于FMM的修补算法可以快速完成修补,但存在行进方向和边缘信息保持的问题.对此进行了改进,采用MFM方法,并引入扩散张量.实验结果表明提出的方法可以达到较高的质量,而且速度快.     

Detail retaining convolutional neural network for image denoising

Compared with the traditional image denoising method, although the convolutional neural network (CNN) has better denoising performance, there is an important issue that has not been well resolved: the residual image obtained by learning the difference between noisy image and clean image pairs contains abundant image detail information, resulting in the serious loss of detail in the denoised image. In this paper, in order to relearn the lost image detail information, a mathematical model is deducted from a minimization problem and an end-to-end detail retaining CNN (DRCNN) is proposed. Unlike most denoising methods based on CNN, DRCNN is not only focus to image denoising, but also the integrity of high frequency image content. DRCNN needs less parameters and storage space, therefore it has better generalization ability. Moreover, DRCNN can also adapt to different image restoration tasks such as blind image denoising, single image superresolution (SISR), blind deburring and image inpainting. Extensive experiments show that DRCNN has a better effect than some classic and novel methods.     

基于成像时刻校准的混合域TDICMOS成像技术研究

随着航天遥感领域超高分辨率成像电子学中行频的不断提升，一个积分时间内光生电荷数量逐渐减少，弱光成像能力有所下降，电子学中需要采用增大时间延迟积分级数的方法弥补能量的不足。传统数字域累加探测器有着引入过多噪声与帧频受限的多项弊端，而电荷域探测器的超大级数累加会带来电荷转移效率的下降和图像的混叠。基于此，文章在采用低功耗、高集成度TDICMOS基础上，提出了一种基于电荷域和数字域混合的新型累加方式，并对影响弱光成像像质水平的主要指标进行分析。随后针对混合域累加方式下多感光单元间的像质退化，提出一种基于图像配准的成像时刻校准方法，通过多片感光单元间隔测量和成像时刻时序微调有效改善了大积分级数电荷运动与景物运动的失配程度。最后通过滚筒测试验证了成像时刻校准方法的有效性，通过性能测试验证了混合域成像在弱光照下探测能力的提升。结果表明，文中所提方法有效地解决了TDI型探测器的主要瓶颈，为超高分辨率遥感相机提供了有效的解决方案。     

Generative image inpainting with salient prior and relative total variation

Image inpainting is an important research direction of image processing. The generative adversarial network (GAN), which can reconstruct new reasonable content in the corrupted region, is the most interesting tool in current inpainting technologies. However, the previous deep methods generally need to be pre-added the binary mask representing the corruption location as the extra input. A novel inpainting algorithm which does not require additional external labels is proposed in this paper. The algorithm consists of two parts: corruption recognition module and content inpainting module, which can recognize and fill random corruption. In the recognizer, the salient object from the uncorrupted region is used as the prior for distinguishing corruption. In the inpainting module, a two-stage network is applied to reconstruct the image from coarse content to texture details. To avoid the misdetection in recognition which has a negative impact on the restoration in inpainting, we perform relative total variational filtering on the corrupted image, and use the salient map as the supervision of detail reconstruction. Qualitative and quantitative experiments on multiple datasets verify the effectiveness of our recognition module, the competitive advantage of our inpainting module, and the enlightening significance of our total algorithm in image inpainting.     

基于加权优先级和分类匹配的图像修复方法

待修复像素优先级的计算及最佳匹配块的确定是基于纹理合成图像修复方法的两个基本环节,传统方法不仅难于确定优先级计算中的置信度,而且难于搜索到最佳匹配块.提出了一种基于加权优先级和分类匹配的图像修复方法,该方法在优先级模型中,引入指数函数和正规化函数分别优化置信度和数据项,使得计算的优先级更加客观,从而使修复顺序更加合理.基于此,将结构信息作为搜索匹配块的一个度量因子,采用分类筛选方式,选取最佳匹配块.实验结果表明,所提方法在获得良好修复效果的前提下缩短了修复时间.     

3D reconstruction with auto-selected keyframes based on depth completion correction and pose fusion

Dense 3D reconstruction is required for robots to safely navigate or perform advanced tasks. The accurate depth information of the image and its pose are the basis of 3D reconstruction. The resolution of depth maps obtained by LIDAR and RGB-D cameras is limited, and traditional pose calculation methods are not accurate enough. In addition, if each image is used for dense 3D reconstruction, the dense point clouds will increase the amount of calculation. To address these issues, we propose a 3D reconstruction system. Specifically, we propose a depth network of contour and gradient attention, which is used to complete and correct depth maps to obtain high-resolution and high-quality depth maps. Then, we propose a method of fusion of traditional algorithms and deep learning for pose estimation to obtain accurate localization results. Finally, we adopt the method of autonomous selection of keyframes to reduce the number of keyframes, the surfel-based geometric reconstruction is performed to reconstruct the dense 3D environment. On the TUM RGB-D, ICL-NIUM, and KITTI datasets, our method significantly improves the quality of the depth maps, the localization results, and the effect of 3D reconstruction. At the same time, we have also accelerated the speed of 3D reconstruction.     

Application of graph neural network and feature information enhancement in relation inference of sparse knowledge graph

At present, knowledge embedding methods are widely used in the field of knowledge graph (KG) reasoning, and have been successfully applied to those with large entities and relationships. However, in research and production environments, there are a large number of KGs with a small number of entities and relations, which are called sparse KGs. Limited by the performance of knowledge extraction methods or some other reasons (some common-sense information does not appear in the natural corpus), the relation between entities is often incomplete. To solve this problem, a method of the graph neural network and information enhancement is proposed. The improved method increases the mean reciprocal rank (MRR) and Hits@3 by 1.6% and 1.7%, respectively, when the sparsity of the FB15K-237 dataset is 10%. When the sparsity is 50%, the evaluation indexes MRR and Hits@10 are increased by 0.8% and 1.8%, respectively.     

基于子带信号处理的太赫兹SAR运动误差补偿方法

太赫兹合成孔径雷达（terahertz synthetic aperture radar,THz-SAR）波长短,可以工作在灰尘、雾霾等复杂战场环境。由于工作波长短,平台微小振动误差会使图像散焦严重。受高频振动误差的影响,目标除了自身响应外,沿方位向还会出现成对回波,给传统相位梯度自聚焦（phase gradient autofocus,PGA）算法强点提取和加窗操作增加了难度,影响运动误差提取精度。为了提高PGA算法精度,提出一种基于子带分解共轭消除相位误差的THz-SAR运动补偿方法,利用该概念可以重建出等效频率更低的SAR图像。由于相同运动误差对等效低频SAR图像影响大大降低,孤立强散射体散焦减弱,有利于强点提取和加窗。利用该图像结合PGA算法可实现运动误差信息的有效提取,并最终用于对原始THz-SAR图像的运动误差补偿。利用仿真数据和0.3 THz雷达实测数据进行了原理验证实验,结果验证了所提算法的有效性。     

A method of image quality assessment for compressive sampling video transmission

Based on compressive sampling transmission model, we demonstrate here a method of quality evaluation for the reconstruction images, which is promising for the transmission of unstructured signal with reduced dimension. By this method, the auxiliary information of the recovery image quality is obtained as a feedback to control number of measurements from compressive sampling video stream. Therefore, the number of measurements can be easily derived at the condition of the absence of information sparsity, and the recovery image quality is effectively improved. Theoretical and experimental results show that this algorithm can estimate the quality of images effectively and is in well consistency with the traditional objective evaluation algorithm.