图像传感器

美国纽约州的Eastnan Kodak公司生产的Kodak Digital Science KAF—1401E型图像传感器,分辨率为1320×1034。像元尺     

图像传感技术中的图像信号处理

伴随着计算机技术的飞速发展,图像处理技术的研究和应用逐渐受到人们的广泛关注。正如我们日常所见的视频监控等全数字图像信号处理设备,会受到天气、系统自身等因素的限制和影响,图像信号处理效果往往达不到最佳效果。文章就图像传感技术的基本功能和特征进行分析,就其一些基本的技术实现展开探讨。     

CMOS图像传感器赶超CCD图像传感器

比较了CMOS图像传感器与CCD图像传感器的优缺点，分析了CMOS图像传感器的结构、研制现状、应用及市场前景。提出了随着CMOS图像传感器技术的发展，CMOS图像传感器可以代替CCD图像传感器，并对其发展趋势作了预见。     

遥感图像的图像配准方法

图像配准技术是近年来发展迅速的图像处理技术之一，是图像拼接、信息融合不可缺少的步骤。本文对遥感图像的配准进行了总结和归纳，介绍了几种常见的图像配准方法，并根据高光谱图像和高空间分辨率图像的特点指出了适合不同类型遥感图像的配准方法。     

基于图像分解的图像修复算法

针对传统变分模型在修复图像时易产生“阶梯效应”与细节模糊等问题,提出了一种基于图像分解的自适应二阶总广义变分和分数阶变分的图像修复算法。首先将待修复的目标图像分解为卡通部分与纹理部分,其中卡通对应目标图像的低、中频部分,因此利用抑制“阶梯效应”较好的二阶总广义变分模型对其进行修复;纹理对应其高频部分,因此利用对细节部分有增强效果的分数阶变分模型对其进行修复。由于文中所提到的修复模型均与线性鞍点结构下求取最优值的模型类似,因此在算法上均采用基于预解式的原始对偶算法对新模型进行求解。另外,为了取得更好的修复效果,文中设计了一个边缘指示算子来自适应地控制新模型的扩散,以更好地保护修复图像的边缘细节。实验结果表明:相比传统的TV、TGV修复模型,新模型的修复效果在主观视觉上显得更加自然,且在峰值信噪比与相关系数等客观评价指标上均有提高。     

利用图像纹理特征的图像检索

随着多媒体技术的发展，大容量图像库得到了广泛的应用，基于内容的图像检索（CBIR）技术则是进行管理和检索的有效手段。介绍了利用图像的纹理特征进行图像检索的方法、具体算法和CBIR系统的实现，给出了试验结果。     

无线图像应用及下一代图像技术

随着多媒体通信业务的迅速发展,人们对移动业务也提出了新的要求。简要介绍了第三代移动通信中的图像业务,其中包括一项重要的业务－多媒体消息业务,并在此基础上分析了新业务以及多业务网络的发展对图像应用和格式所提出的一些要求,最后结合这些要求详细介绍了新制度的ISO／IEC图像标准－JPEG2000     

图像均衡器

在图像均衡电路中,采用由运算放大器形成的模拟电感和电容组成的串联谐振电路,可以做到使特定频率的响应上升或下降。即,当电位器移向CUT侧时,串联谐振回路与信号回路并联连接,使谐振频率上的信号衰减。反之当移向BOOST侧时,串联谐振回     

图像显示装置

本发明提供在FED方式的图像显示装置中，降低流向隔板的电流的、消耗电量低的图像显示装置。本发明的图像显示装置，具备设置有发射电子的多个电子发射元件的阴极基板、与前述阴极基板相对地配置的具有荧光体和金属衬垫的阳极基板、     

Single-Image Super-Resolution Reconstruction via Learned Geometric Dictionaries and Clustered Sparse Coding

Recently, single image super-resolution reconstruction (SISR) via sparse coding has attracted increasing interest. In this paper, we proposed a multiple-geometric-dictionaries-based clustered sparse coding scheme for SISR. Firstly, a large number of high-resolution (HR) image patches are randomly extracted from a set of example training images and clustered into several groups of "geometric patches," from which the corresponding "geometric dictionaries" are learned to further sparsely code each local patch in a low-resolution image. A clustering aggregation is performed on the HR patches recovered by different dictionaries, followed by a subsequent patch aggregation to estimate the HR image. Considering that there are often many repetitive image structures in an image, we add a self-similarity constraint on the recovered image in patch aggregation to reveal new features and details. Finally, the HR residual image is estimated by the proposed recovery method and compensated to better preserve the subtle details of the images. Some experiments test the proposed method on natural images, and the results show that the proposed method outperforms its counterparts in both visual fidelity and numerical measures.     

采用GSM模型进行稀疏表示的SAR图像降斑算法

针对SAR图像降斑过程中会产生过平滑现象及相干斑的滤除不彻底等问题,提出了稀疏结构符合高斯比例混合（Gaussian Scale Mixture,GSM）模型的SAR图像降斑算法。根据贝叶斯原理以及相干斑的统计特性推导该算法的数学模型,在块匹配过程中使用概率而不是欧式距离进行权重衡量,根据图像块之间的结构相似度,可以有效区分同质区与异质区,并得到图像块的较优均值估计。使用PCA字典学习方法对每个图像块进行子字典训练,实现同步稀疏编码（Simultaneous Sparse Coding,SSC）,数学模型的求解利用迭代正则化方法。分别使用合成场景SAR图像及真实场景SAR图像对算法进行验证,实验表明,相比于目前已提出的PPB算法、SAR-BM3D算法及FANS算法,该算法能有效提高等效视数,在滤除相干斑的同时很好地保留了图像的局部结构特性与纹理特征。      

Fast super-resolution algorithm using rotation-invariant ELBP classifier and hierarchical pattern matching

This paper proposes a fast super-resolution (SR) algorithm using content-adaptive two-dimensional (2D) finite impulse response (FIR) filters based on a rotation-invariant classifier. The proposed algorithm consists of a learning stage and an inference stage. In the learning stage, we cluster a sufficient number of low-resolution (LR) and high-resolution (HR) patch pairs into a specific number of groups using the rotation-invariant classifier, and choose a specific number of dominant clusters. Then, we compute the optimal 2D FIR filter(s) to synthesize a high-quality HR patch from an LR patch per cluster, and finally store the patch-adaptive 2D FIR filters in a dictionary. Also, we present a smart hierarchical addressing method for effective dictionary exploration in the inference stage. In the inference stage, the ELBP of each input LR patch is extracted in the same way as the learning stage, and the best matched FIR filter(s) to the input LR patch is found from the dictionary by the hierarchical addressing. Finally, we synthesize the HR patch by using the optimal 2D FIR filter. The experimental results show that the proposed algorithm produces better HR images than the existing SR methods, while providing fast running time.     

基于邻域一致性的极化SAR图像仿射配准

极化SAR图像的配准是极化SAR图像处理的基础,需要具备较高的精度与速度。基于深度学习的极化SAR图像配准大多数是结合图像块特征的匹配与基于随机抽样一致性的参数迭代估计来实现的。目前尚未实现端到端的基于深度卷积神经网络的一步仿射配准。该文提出了一种基于弱监督学习的端到端极化SAR图像配准框架,无需图像切块处理或迭代参数估计。首先,对输入图像对进行特征提取,得到密集的特征图。在此基础上,针对每个特征点保留k对相关度最高的特征点对。之后,将该4D稀疏特征匹配图输入4D稀疏卷积网络,基于邻域一致性进行特征匹配的过滤。最后,结合输出的匹配点对置信度,利用带权最小二乘法进行仿射参数回归,实现图像对的配准。该文采用RADARSAT-2卫星获取的德国Wallerfing地区农田数据以及PAZ卫星获取的中国舟山港口地区数据作为测试图像对。通过对升降轨、不同成像模式、不同极化方式、不同分辨率的极化SAR图像对的配准测试,并与4种现有方法进行对比,验证了该方法具有较高的配准精度与较快的速度。      

基于结构化稀疏表达的高分辨率光学遥感图像的船只检测

为克服近岸船只检测中复杂港内背景干扰和基于深度学习算法的大视场光学遥感图像标注工作量大的困难,本文提出了基于小样本集的结构化稀疏表达方法来实现近岸船只检测的算法。构建由近岸船只目标,背景干扰信息和误差矩阵等三部分子字典组成的结构化稀疏表达字典,经小样本集的字典训练过程生成判别性稀疏编码。首先将多方向近岸船只目标样本与港内复杂背景信息样本经过HOG特征提取和PCA分析对原子进行初始化,然后使用K-SVD和LASSO算法对字典进行训练。在字典中引入误差矩阵对样本的类内差异进行表示,增强了稀疏编码的判别能力和系统鲁棒性。最后提出船只目标区域提取的置信度计算方法,对生成的结构化稀疏编码进行判别,提取船只目标区域,实现船只检测。通过对不同尺寸字典模型、引入误差矩阵前后的结构化稀疏表达模型进行实验,实验结果表明提出的引入误差矩阵的结构化稀疏表达方法的有效性,以及在小样本集下比现有技术方法具有更好的检测性能。      

基于可区分性字典学习模型的极化SAR图像分类

极化SAR图像分类是一个高维非线性映射问题,稀疏表示(CS)对于解决此类问题具有很大潜力。字典学习在基于CS的分类中起到重要作用。本文提出了一种新的字典学习模型,用于增强字典的区分能力,使其更适合极化SAR图像分类。提出的模型根据字典中两类子字典在分类中的作用对其相应的表达系数施加不同的稀疏约束。为使共同子字典能够抓住所有类共享的特征,对其相应系数施加稀疏约束,为使类专属子字典能够抓住类内独享的局部和全局结构特征,对其相应系数同时施加稀疏和低秩约束。由于共同子字典表达所有类共享的特征,我们以测试样本在类专属子字典上的重建误差作为准则进行分类。本文在AIRSAR的Flevoland数据集上对此算法进行验证,实验结果验证了算法的有效性。      

Image super-resolution via sparse representation over multiple learned dictionaries based on edge sharpness

A new algorithm for single-image super-resolution based on selective sparse representation over a set of coupled dictionary pairs is proposed. Patch sharpness measure for high- and low-resolution patch pairs defined via the magnitude of the gradient operator is shown to be approximately invariant to the patch resolution. This measure is employed in the training stage for clustering the training patch pairs and in the reconstruction stage for model selection. For each cluster, a pair of low- and high-resolution dictionaries is learned. In the reconstruction stage, the sharpness measure of a low-resolution patch is used to select the cluster it belongs to. The sparse coding coefficients of the patch over the selected low-resolution cluster dictionary are calculated. The underlying high-resolution patch is reconstructed by multiplying the high-resolution cluster dictionary with the calculated coefficients. The performance of the proposed algorithm is tested over a set of natural images. PSNR and SSIM results show that the proposed algorithm is competitive with the state-of-the-art super-resolution algorithms. In particular, it significantly out-performs the state-of-the-art algorithms for images with sharp edges and corners. Visual comparison results also support the quantitative results.     

FuseKin: Weighted image fusion based kinship verification under unconstrained age group

Kinship verification using facial images is mainly performed with a single face sample per person. To perform with a single sample, it is very difficult to specify an age group where kin pairs may have higher similarities. To address the above problem, we propose a novel weighted multi sample fusion (WMSF) method. The proposed WMSF method combines kin signals present in multiple samples per person (MSPP) to form a FuseKin image. To select the most discriminant features from the extracted feature vector, we propose a patch based discriminative analysis (PDA) method. Weights are calculated using the PDA method so as to reduce the discrimination between positive FuseKin pairs. Experiments were conducted on two different datasets which contain multiple face image samples per person, namely Family101 and Family in the Wild (FIW) to validate the performance of the proposed methods. Our method achieves competitive results as compared to other state-of-the-art methods.     

Low-Resolution and Low-Quality Face Super-Resolution in Monitoring Scene via Support-Driven Sparse Coding

Monitoring cameras are now widely used to monitor everything from a room in a house to an entire warehouse. However, in real monitoring scenarios, a variety of factors, such as underexposure, optical blurring, defocusing, have an impact on the quality of images, which leads to low-quality and low-resolution (LR) of the individual of interest. Reconstruction of a high-resolution (HR) face image with detailed facial features, from a LR observation based on a set of HR and LR training image pairs, plays an important role in computer vision and face image analysis applications. To super-resolve an HR face given a LR face image, the key issue is how to effectively encode the LR image patch. However, due to stability and accuracy issues, the coding approaches proposed so far are far from satisfactory. In this paper, we present a novel sparse coding method via exploiting the support information on the coding coefficients. According to the distances between the input patch and bases in the dictionary, we first assign different weights to the coding coefficients and then obtain the coding coefficients by solving a weighted sparse problem. Experiments on commonly used databases and some face images on the real monitoring conditions demonstrate that our method outperforms state-of-the-art.     

Precise Geometric Registration of Aerial Imagery and LIDAR Data

In this paper, we develop a registration method to eliminate the geometric inconsistency between the stereo‐images and light detection and ranging (LIDAR) data obtained by an airborne multisensor system. This method consists of three steps: registration primitive extraction, correspondence establishment, and exterior orientation parameter (EOP) adjustment. As the primitives, we employ object points and linked edges from the stereo‐images and planar patches and intersection edges from the LIDAR data. After extracting these primitives, we establish the correspondence between them, being classified into vertical and horizontal groups. These corresponding pairs are simultaneously incorporated as stochastic constraints into aerial triangulation based on the bundle block adjustment. Finally, the EOPs of the images are adjusted to minimize the inconsistency. The results from the application of our method to real data demonstrate that the inconsistency between both data sets is significantly reduced from the range of 0.5 m to 2 m to less than 0.05 m. Hence, the results show that the proposed method is useful for the data fusion of aerial images and LIDAR data.