The Multi-Focus-Image-Fusion Method Based on Convolutional Neural Network and Sparse Representation

Multi-focus-image-fusion is a crucial embranchment of image processing. Many methods have been developed from different perspectives to solve this problem. Among them, the sparse representation (SR)-based and convolutional neural network (CNN)-based fusion methods have been widely used. Fusing the source image patches, the SR-based model is essentially a local method with a nonlinear fusion rule. On the other hand, the direct mapping between the source images follows the decision map which is learned via CNN. The fusion is a global one with a linear fusion rule. Combining the advantages of the above two methods, a novel fusion method that applies CNN to assist SR is proposed for the purpose of gaining a fused image with more precise and abundant information. In the proposed method, source image patches were fused based on SR and the new weight obtained by CNN. Experimental results demonstrate that the proposed method clearly outperforms existing state-of-the-art methods in addition to SR and CNN in terms of both visual perception and objective evaluation metrics, and the computational complexity is greatly reduced. Experimental results demonstrate that the proposed method not only clearly outperforms the SR and CNN methods in terms of visual perception and objective evaluation indicators, but is also significantly better than other state-of-the-art methods since our computational complexity is greatly reduced.     

Structural similarity based single image super-resolution with nonlocal regularization

Recently, sparse coding based image super-resolution has attracted increasing interests. This paper proposes an improved image super-resolution method, by incorporating structural similarity (SSIM) index and nonlocal regularization into the framework of image super-resolution via sparse coding. Firstly, an algorithm of combining SSIM based sparse coding and K-SVD is proposed to train the high resolution (HR) and low resolution (LR) dictionary pairs. And then, the sparse representations of observed LR image are sought to reconstruct the HR image with the trained LR and HR dictionary pairs by exploiting nonlocal self-similarities. Experimental results demonstrate the effectiveness of the proposed method, both in its visual effects and in quantitative terms.     

基于GoogLeNet和空间谱变换的高光谱图像超分辨率方法

为了提高高光谱图像的空间分辨率,提出了一种基于GoogLeNet和空间谱变换的高光谱图像超分辨率(SR)方法.设计出遥感图像的光谱SR框架,对图像中不同反射光谱进行提取;采用GoogLeNet的稀疏编码对粗像素光谱进行放大,并投影到高分辨率字典上,将潜在SR表示进行反转,以获得超分辨光谱;为了提高图像重构的保真度,利用...     

Single-image super-resolution with joint-optimization of TV regularization and sparse representation

A super-resolution (SR) reconstruction framework is proposed using regularization restoration combined with learning-based resolution enhancement via sparse representation. With the viewpoint of conventional learning methods, the original image can be split into low frequency (LF) and high frequency (HF) components. The reconstruction mainly focuses on the process of HF part, while the LF one is founded simply by typical interpolation function. For the severely blurred single-image, we first use regularization restoration technology to recover it. Then the regularized output remarkably betters the quality of LF used in traditional learning-based methods. Last, image resolution enhancement with characteristic of edge preserving can implement based on the acquired relatively sharp intermediate image and the pre-constructed over-complete dictionary for sparse representation. Specifically, the regularization can favorably weaken the dependence of atoms on the course of degradation. With both techniques, we can noticeably eliminate the blur and the edge artifacts in the enlarged image simultaneously. Various experimental results demonstrate that the proposed approach can produce visually pleasing resolution for severely blurred image.     

基于字典学习的稠密光场重建算法

相机阵列是获取空间中目标光场信息的重要手段,采用大规模密集相机阵列获取高角度分辨率光场的方法增加了采样难度和设备成本,同时产生的大量数据的同步和传输需求也限制了光场采样规模.为了实现稀疏光场采样的稠密重建,本文基于稀疏光场数据,分析同一场景多视角图像的空间、角度信息的关联性和冗余性,建立有效的光场字典学习和稀疏编码数学模型,并根据稀疏编码元素间的约束关系,建立虚拟角度图像稀疏编码恢复模型,提出变换域稀疏编码恢复方法,并结合多场景稠密重建实验,验证提出方法的有效性.实验结果表明,本文方法能够对场景中的遮挡、阴影以及复杂的光影变化信息进行高质量恢复,可以用于复杂场景的稀疏光场稠密重建.本研究实现了线性采集稀疏光场的稠密重建,未来将针对非线性采集稀疏光场的稠密重建进行研究,以推进光场成像在实际工程中的应用.     

Split Bregman iteration solution for sparse optimization in image restoration

It is always a challenging task to develop effective and accurate models for robust image restoration. In this paper, the family of sparse and redundant representation frameworks is considered as an alternative for the above problem. The principle of the family is expatiated on the development and research progress. Two well-known denoising methods are presented and analyzed on their properties. The K-SVD algorithm is an effective method for sparse representation. The iteratively approximate algorithms are always used for the solution of sparse coding operations. Here, a convexification of the l₀ norm to the l₁ norm is adopted in the implementation of K-SVD method. Then a split Bregman iteration solution is proposed for l₁ regularization problems in the performance of the sparse representation of the K-SVD algorithm. The split Bregman iterative method is well studied and fused into the famous K-SVD method. The PSNR (Peak Signal to Noise Ratio) and MSSIM (Mean Structural Similarity) are used to evaluate the performance of those methods. Experimental results on different types of images indicate that our proposed method not only achieve comparable results with the state of art methods, but also make the original method more efficient. Besides, it also provides a valuable and promising reference for image restoration techniques.     

一种基于稀疏编码空间金字塔匹配的图像分类算法

图像分类技术是近年来计算机视觉领域中的研究热点,在移动互联网领域中取得了成功应用。提出了一种基于稀疏编码空间金字塔匹配的图像分类算法。该方法首先对图像的SIFT特征进行稀疏编码,替代了传统的矢量量化方法,可以有效降低量化误差,构建更为准确的图像表征方式,然后结合空间金字塔匹配算法采用线性分类器对图像进行分类识别。在标准测试图像数据库上的实验结果表明,相比BOF和SPM方法,该算法可以将图像分类准确率提高4%~12%。     

Synthetic Aperture Radar Image Despeckling Based on Multi-Weighted Sparse Coding

Synthetic aperture radar (SAR) images are inherently degraded by speckle noise caused by coherent imaging, which may affect the performance of the subsequent image analysis task. To resolve this problem, this article proposes an integrated SAR image despeckling model based on dictionary learning and multi-weighted sparse coding. First, the dictionary is trained by groups composed of similar image patches, which have the same structural features. An effective orthogonal dictionary with high sparse representation ability is realized by introducing a properly tight frame. Furthermore, the data-fidelity term and regularization terms are constrained by weighting factors. The weighted sparse representation model not only fully utilizes the interblock relevance but also reflects the importance of various structural groups in despeckling processing. The proposed model is implemented with fast and effective solving steps that simultaneously perform orthogonal dictionary learning, weight parameter updating, sparse coding, and image reconstruction. The solving steps are designed using the alternative minimization method. Finally, the speckles are further suppressed by iterative regularization methods. In a comparison study with existing methods, our method demonstrated state-of-the-art performance in suppressing speckle noise and protecting the image texture details.     

Optimization-based image reconstruction in x-ray computed tomography by sparsity exploitation of local continuity and nonlocal spatial self-similarity

The additional sparse prior of images has been the subject of much research in problems of sparse-view computed tomography(CT) reconstruction. A method employing the image gradient sparsity is often used to reduce the sampling rate and is shown to remove the unwanted artifacts while preserve sharp edges, but may cause blocky or patchy artifacts.To eliminate this drawback, we propose a novel sparsity exploitation-based model for CT image reconstruction. In the presented model, the sparse representation and sparsity exploitation of both gradient and nonlocal gradient are investigated.The new model is shown to offer the potential for better results by introducing a similarity prior information of the image structure. Then, an effective alternating direction minimization algorithm is developed to optimize the objective function with a robust convergence result. Qualitative and quantitative evaluations have been carried out both on the simulation and real data in terms of accuracy and resolution properties. The results indicate that the proposed method can be applied for achieving better image-quality potential with the theoretically expected detailed feature preservation.     

Structural Smoothing Low-Rank Matrix Restoration Based on Sparse Coding and Dual-Weighted Model

Group sparse coding (GSC) uses the non-local similarity of images as constraints, which can fully exploit the structure and group sparse features of images. However, it only imposes the sparsity on the group coefficients, which limits the effectiveness of reconstructing real images. Low-rank regularized group sparse coding (LR-GSC) reduces this gap by imposing low-rankness on the group sparse coefficients. However, due to the use of non-local similarity, the edges and details of the images are over-smoothed, resulting in the blocking artifact of the images. In this paper, we propose a low-rank matrix restoration model based on sparse coding and dual weighting. In addition, total variation (TV) regularization is integrated into the proposed model to maintain local structure smoothness and edge features. Finally, to solve the problem of the proposed optimization, an optimization method is developed based on the alternating direction method. Extensive experimental results show that the proposed SDWLR-GSC algorithm outperforms state-of-the-art algorithms for image restoration when the images have large and sparse noise, such as salt and pepper noise.     

Single infrared image super-resolution combining non-local means with kernel regression

In many infrared imaging systems, the focal plane array is not sufficient dense to adequately sample the scene with the desired field of view. Therefore, there are not enough high frequency details in the infrared image generally. Super-resolution (SR) technology can be used to increase the resolution of low-resolution (LR) infrared image. In this paper, a novel super-resolution algorithm is proposed based on non-local means (NLM) and steering kernel regression (SKR). Based on that there are a large number of similar patches within an infrared image, NLM method can abstract the non-local similarity information and then the value of high-resolution (HR) pixel can be estimated. SKR method is derived based on the local smoothness of the natural images. In this paper the SKR is used to give the regularization term which can restrict the image noise and protect image edges. The estimated SR image is obtained by minimizing a cost function. In the experiments the proposed algorithm is compared with state-of-the-art algorithms. The comparison results show that the proposed method is robust to the noise and it can restore higher quality image both in quantitative term and visual effect.     

基于非冗余轮廓波的图像质量可伸缩编码算法

提出一种用非冗余轮廓波的中低比特率图像质量可伸缩编码算法。该算法采用双正交小波分解和方向滤波器组(DFB)实现图像的非冗余稀疏表示, 不但具有轮廓波对图像中线状奇异性边缘和纹理细节的稀疏表示特点, 而且克服了轮廓波变换系数4/3冗余的缺点。算法中对图像非冗余轮廓波系数各子带系数分布进行统计分析, 通过对变换系数的重新组合, 构造了有利于图像编码的空间方向树结构, 并统计验证了其零树特性, 采用分级树集合分裂和阈值量化达到图像质量可伸缩的嵌入式编码。实验结果表明,其解码算法在中低比特率压缩情况下, 压缩后重构图像的感知质量明显优于小波域SPIHT,JPEG2000编码标准, 峰值信噪比PSNR值与JPEG2000相当, 而图像纹理和边缘细节的视觉效果优于JPEG2000和小波域SPIHT算法。     

汽车自动变速箱油的近红外光谱识别研究

利用自编码网络(autoencoder network, AN)流形学习和稀疏表示(sparse representation, SR)方法对汽车变速箱油进行近红外光谱品种识别研究。以壳牌、美孚、嘉实多、上海大众和上海通用五种变速箱油为对象,利用AN方法对600～1800 nm近红外光谱数据进行非线性降维,获取10个特征变量。每种变速箱油选取30个样本(共150个样本)作为训练样本,每种30个样本(共150个样本)作为测试样本。所有训练样本的特征变量组成了稀疏表示方法的整体训练样本矩阵,将变速箱油品种分类识别问题转化为一个求解待识别测试样本对于整体训练样本矩阵的稀疏表示问题,通过求解L-1范数意义下的最优化问题来实现。经过主成分分析(principal component analysis,PCA)和AN降维后,分别利用线性判断分析法(linear discriminant analysis,LDA)、偏最小二乘支持向量机法(least squares-support vector machine,LS-SVM)和本文提出的稀疏表示分类算法进行分类比较。结果表明,结合自编码网络和稀疏表示方法对五种汽车变速箱油品种的平均识别准确率达97.33%,为汽车变速箱油品种近红外光谱快速准确识别提供了有效的新途径。     

An occlusion-adaptive tracker based on sparse representation using alternating direction method of multipliers

Recently, sparse representation has been applied to visual tracking with satisfactory performance. However, partial occlusion and computational complexity are two main obstructions in developing sparse-based tracking. In this paper, a simple yet robust tracker based on patch-based sparse representation is proposed. An adaptive motion model, including adaptive sampling regions and adaptive particle numbers, is proposed to improve the sampling efficiency. A self-adjustable segmentation approach is proposed to segment the target into local patches. A patch-based observation model, which is occlusion-adaptive, is constructed by solving a set of L1-regularized least squares problems. The L1-regularized least squares problem is solved using the alternating direction method of multipliers (ADMM). Both quantitative and qualitative experiments are conducted on several challenging image sequences and the comparisons with several state-of-the-art trackers demonstrate the effectiveness and efficiency of our tracker.     

Image restoration with dual-prior constraint models based on Split Bregman

In order to utilizing the local and non-local information in the image, we proposed a novel sparse scheme for image restoration in this paper. The new scheme includes two important contributions. The first one is that we extended the image prior model in conventional total variation to the dual-prior models for combining the local smoothness and non-local sparsity under regularization framework. The second one is we developed a modified iterative Split Bregman majorization method to solve the objective function with dual-prior models. The experimental results show that the proposed scheme achieved the state-of-the-art performance compared to the current restoration algorithms.     

Novel classification rule of two-phase test sample sparse representation

In this paper, a novel classifier based on two-phase test sample sparse representation (TPTSSR) classifier and coarse k nearest neighbor (C-kNN) classifier, called novel classification rule of two-phase test sample sparse representation (NCR-TPTSSR) classifier, is proposed for image recognition. Being similar to TPTSSR classifier and C-kNN classifier, NCR-TPTSSR classifier also uses the two phases to classify the test sample. However, the classification rule of NCR-TPTSSR classifier is different to the decision rule of TPTSSR classifier and C-kNN classifier. A large number of experiments on FERET face database, AR face database, JAFFE face database and PolyU FKP database are used to evaluate the proposed algorithm. The experimental results demonstrate that the proposed method achieves better recognition rate than TPTSSR classifier, C-kNN classifier, nearest feature center (NFC) classifier, nearest feature line (NFL) classifier, nearest neighbor (NN) and so on.     

Optimum adaptive array stochastic resonance in noisy grayscale image restoration

Considering the widespread noise interference in the two-dimensional (2D) image transmission processing, we proposed an optimal adaptive bistable array stochastic resonance (SR)-based grayscale image restoration enhancement method under low peak signal-to-noise ratio (PSNR) environments. In this method, the Hilbert scanning is adopted to reduce the dimension of the original grayscale image. The 2D image signal is converted into a one-dimensional (1D) binary pulse amplitude modulation (BPAM) signal. Meanwhile, we use the adaptive bistable array SR module to enhance the 1D low SNR BPAM signal. In order to obtain the restored image, we transform the enhanced BPAM signal into a 2D grayscale image signal. Simulation results show that the proposed method significantly outperforms the classical image restoration methods (i.e., mean filter, Wiener filter and median filter) both on the grayscale level and the PSNR of the restored image, particularly in a low PSNR scenario. Larger array size brings better image restoration effect.     

基于Contourlet变换的迭代图像复原算法

考虑到contourlet变换的多尺度多方向性以及对二维图像具有比小波变换更好的稀疏表示特性,提出了一种基于contourlet变换的图像复原算法.算法采用边界优化的方法,通过类期望最大化算法在contourlet域进行迭代计算,并最终获得惩罚似然函数的最优解.实验结果表明.与传统的基于小波变换的同类图像复原算法相比,基于contourlet变换的复原算法在保持了较低的运算代价的同时,更好地保护了图像的边缘和细节信息,峰值信噪比有0.6 dB～0.8 dB左右的提高.     

结合稀疏编码和空间约束的红外图像聚类分割研究

提出了结合稀疏编码和空间约束的红外图像聚类分割新算法, 在稀疏编码的基础上融合聚类算法, 扩展了传统的基于K-means聚类的图像分割方法. 结合稀疏编码的聚类分割算法能有效融合图像的局部信息, 便于利用像素之间的内在相关性, 但是对于分割会出现过分割和像素难以归类的问题.为此, 在字典的学习过程中, 将原子的聚类算法引入其中, 有助于缩减字典中原子所属类别的数目, 防止出现过分割; 考虑到像素及其邻域像素具有类别属性一致性的特点, 引入了空间类别属性约束信息, 并给出了一种交替优化算法. 联合学习字典、稀疏系数、聚类中心和隶属度, 将稀疏编码系数同原子对聚类中心的隶属程度相结合, 构造像素归属度来判断像素所属的类别. 实验结果表明, 该方法能够有效提高红外图像重要区域的分割效果, 具有较好的鲁棒性. 关键词： 图像分割 稀疏编码 聚类 空间约束     

融合自适应稀疏表示和相关系数的高光谱伪装分类方法

目前比较成熟的高光谱成像手段有卫星遥感和航空成像技术,这两种成像方式侦察时间大致相同,入射光方向基本一致,因而地物的光谱曲线比较固定;在陆基条件下,地物的光谱曲线受成像环境的影响凸显,因此应该对适用于陆基条件下的高光谱图像分类方法进行研究。在陆基高光谱图像中,对每个地物进行类型以及种类的判别有利于后续对目标的识别和处理,不同于传统遥感图像分类,陆基条件下的高光谱图像目标分类训练样本不仅较难获得,并且在陆基条件下的高光谱图像中,训练样本之间的相关性随着目标类型、探测器参数以及成像环境等因素时刻发生变化。基于稀疏性表示的分类方法已经被广泛应用于处理图像问题以及各种机器视觉问题。对于陆基高光谱图像来说,基于固定范数约束的稀疏编码策略无法适应陆基条件下高光谱成像多变的环境,而自适应稀疏表示可以根据样本相关性自适应的调节范数约束,相关系数可以提高图像中的破坏因素（阴影、噪声点等）的识别精度。通过引入正则化参数,融合了自适应稀疏表示和相关系数,提出了一种新的高光谱图像分类方法。为了验证所提方法的有效性,分别在绿色植被背景和荒漠背景中设置伪装物,通过不同的分类方法对图像进行分类,实验结果表明,不管是分类精度还是分类一致性,该方法都有明显的优势,可以应用于陆基条件下的高光谱图像分类,为目标分类提供了理论基础。