Image decomposing for inpainting using compressed sensing in DCT domain

Inpainting images with occlusion or corruption is a challenging task. Most existing algorithms are pixel based, which construct a statistical model from image features. However, in these algorithms, the frequency component is not sufficiently addressed. In this paper, we propose a novel algorithm that utilizes compressed sensing （CS） in frequency domain to reconstruct corrupted images. In order to reconstruct image, we first decompose the image into two functions with different basic characteristics - structure component and textual component. We seek a sparse representation for the functions and use the DCT coefficients of this representation to generate an over-complete dictionary. Experimental results on real world datasets demonstrate the efficacy of our method in image inpainting. We compare our method with three state-of-the-art inpalnting algorithms and demonstrate its advantages in terms of both quantitative and qualitative aspects.     

Image categorization using a semantic hierarchy model with sparse set of salient regions

Image categorization in massive image database is an important problem. This paper proposes an approach for image categorization, using sparse set of salient semantic information and hierarchy semantic label tree (HSLT) model. First, to provide more critical image semantics, the proposed sparse set of salient regions only at the focuses of visual attention instead of the entire scene was formed by our proposed saliency detection model with incorporating low and high level feature and Shotton’s semantic texton forests (STFs) method. Second, we also propose a new HSLT model in terms of the sparse regional semantic information to automatically build a semantic image hierarchy, which explicitly encodes a general to specific image relationship. And last, we archived image dataset using image hierarchical semantic, which is help to improve the performance of image organizing and browsing. Extension experimental results showed that the use of semantic hierarchies as a hierarchical organizing framework provides a better image annotation and organization, improves the accuracy and reduces human’s effort.     

Liver Segmentation Technique for CT Images Based on Statistical Analysis

This article presented the automatic diagnosis of liver pathologies and its 3D volume rendering. The first and fundamental step in all these studies is the automatic liver segmentation, which is still an open problem. In this thesis, two automatic methods are described to segment the liver from abdominal CT image data. The first step is image enhancement. Secondly, texture analysis is using standard statistical measures, finally it is a rough mask to segment the liver.     

Cropping and Noise Resilient Steganography Algorithm Using Secret Image Sharing

This paper proposes an image steganography scheme, in which a secret image is hidden into a cover image using a SIS （secret image sharing） scheme. Taking advantage of the fault tolerance property of the （k, n）-threshold SIS, where using any k of n shares （k ≤ n）, the secret data can be recovered without any ambiguity. In order to increase the security of the secret information to digital attacks, the proposed steganography algorithm becomes resilient to cropping and impulsive noise contamination using SIS scheme. Among many SIS schemes proposed until now, the Lin and Chan＇s scheme is selected as the SIS, due to its lossless recovery capability of a large amount of secret data. Stego-image quality and hiding capacity depend on the prim number used in polynomial. The proposed scheme is evaluated from several points of view, such as imperceptibility of the stego-image respect to its original cover image using the PSNR, quality of the extracted secret image, robustness of hidden data to cropping operation, impulsive noise contamination and the combination of both attacks. The evaluation results show a high quality of the extracted secret image from the stego-image when it suffered more than 20% cropping and/or high density noise contamination.     

A theoretical framework for quantum image representation and data loading scheme

Two fundamental problems exist in the use of quantum computation to process an image or signal.The first one is how to represent giant data,such as image data,using quantum state without losing information.The second one is how to load a colossal volume of data into the quantum registers of a quantum CPU from classical electronic memory.Researches on these two questions are rarely reported.Here an entangled state is used to represent an image(or vector)for which two entangled registers are used to store a vector component and its classical address.Using the representation,n1+n2+8 qubits are used to store the whole information of the gray image that has a 2n1×2n2 size at a superposition of states,a feat is not possible with a classic computer.The way of designing a unitary operation to load data,such as a vector(or image),into the quantum registers of a quantum CPU from electronic memory is defined herein as a quantum loading scheme(QLS).In this paper,the QLS with time complexity O(log2N)is presented where N denotes the number of vector components,a solution that would break through the efciency bottleneck of loading data.QLS would enable a quantum CPU to be compatible with electronic memory and make possible quantum image compression and quantum signal processing that has classical input and output.     

Study of the Preprocessing Influence in the Accuracy of Semi-automated Shadow Detection Approach

Shadows may occupy a significant portion of the image mainly in urban scenes. This research has the objective to detect shadows in high resolution orbital images using morphological operators. In order to verify preprocessing contribution in this shadow detection methodology, we have tested the median, morphological, bilateral and mean curvature filters to evaluate which one has the characteristic of mitigate the noise of the images and contribute to enhance detection performance. During the study, 10 panchromatic images of Worldview II satellite from the urban area of Presidente Prudente, in the state of Sao Paulo, were used. According to the shadow detection methodology by mathematical morphology, we checked the accuracy value using the images resulting of the smoothing methods applied in the preprocessing step. Finally, we evaluated all the smoothing levels in order to select the most appropriated according to accuracy and if images preserves the elements of interest. By analyzing the obtained results it is easy to see that the bilateral filter has presented satisfactory results, since it considers the spatial domain in the smooth ing process, despite incorporating the pixels intensity domain as well. Therefore, we can conclude that the bilateral filter is a good alternative considering an adequate choice of the parameters.     

Spatio-Temporal Video Motion Detection Using Marked-Watershed and Morphological Operations

In this article, we describe a moving object detection method developed based on spatio-temporal information and marked-watershed for extracting the moving objects from a video sequence. The algorithm begins with difference image between two adjacent frames and, using the Canny operator on the difference image, determines the initial edge mask for the object in motion Morphological operators are applied to the initial edge map to obtain a temporal segmentation mask of the moving object and binary marker image of the foreground and background, which is subject to the watershed thresholding. The markers are used to modify multi-scale morphological gradient image of the current frame. Finally, the watershed algorithm is performed on the modified gradients to locate the non-stationary objects accurately in the spatial domain of motion frames. Experimental results show that the proposed technique can overcome the shortcoming of over-segmentation of the watershed algorithm. In computationally efficient way, it segments and extracts semantically meaningful objects, which are in slow or fast motion from the video frames of scenes involving complex background. Performance evaluation yields that the detection accuracy can be as high as 98% to 99% for different video sequences.     

A novel binary image representation algorithm by using NAM and coordinate encoding procedure and its application to area calculation

We propose a novel binary image representation algorithm using the non-symmetry and anti-packing model and the coordinate encoding procedure （NAMCEP）. By tak- ing some idiomatic standard binary images in the field of image processing as typical test objects, and by comparing our proposed NAMCEP representation with linear quadtree （LQT）, binary tree （Bintree）, non-symmetry and anti-packing model （NAM） with K-lines （NAMK）, and NAM representa- tions, we show that NAMCEP can not only reduce the aver- age node, but also simultaneously improve the average com- pression. We also present a novel NAMCEP-based algorithm for area calculation and show experimentally that our algo- rithm offers significant improvements.     

SWVFS： a saliency weighted visual feature similarity metric for image quality assessment

In this paper, a saliency weighted visual feature similarity （SWVFS） metric is proposed for full reference im- age quality assessment （IQA）. Instead of traditional spatial pooling strategies, a visual saliency-based approach is em- ployed for better compliance with properties of the human visual system, where the saliency allocation is closely related to the activity of posterior parietal cortex and the pluvial nu- clei of the thalamus. Assuming that the saliency map actually represents the contribution of locally computed visual distor- tions to the overall image quality, the gradient similarity and the textural congruency are merged into the final image qual- ity indicator. The gradient and texture comparison play com- plementary roles in characterizing the local image distortion. Extensive experiments conducted on seven publicly available image databases show that the performance of SWVFS is competitive with the state-of-the-art IQA algorithms.     

Super-Resolution image Reconstruction Based on Iteration and Wavelet Transform

In this paper, we provide a super-resolution image reconstruction algorithm based on wavelet transform. Wavelet transform can separate high frequency and low frequency information of image. The more high frequency information can be obtained by using wavelet transform and the technique of image fusion. Meanwhile, reconstructed super-resolution image is produced by the iterative method. In iteration process, noise of image can be suppressed by applying method of wavelet threshold de-noising. The experiment results show that the algorithm can overcome the disadvantage of the classical interpolation method and effectively improve the resolution and PSNR of the image.     

A survey on distributed compressed sensing： theory and applications

The compressed sensing （CS） theory makes sample rate relate to signal structure and content. CS samples and compresses the signal with far below Nyquist sampling frequency simultaneously. However, CS only considers the intra-signal correlations, without taking the correlations of the multi-signals into account. Distributed compressed sensing （DCS） is an extension of CS that takes advantage of both the inter- and intra-signal correlations, which is wildly used as a powerful method for the multi-signals sensing and compression in many fields. In this paper, the characteristics and related works of DCS are reviewed. The framework of DCS is introduced. As DCS＇s main portions, sparse representation, measurement matrix selection, and joint reconstruction are classified and summarized. The applications of DCS are also categorized and discussed. Finally, the conclusion remarks and the further research works are provided.     

A unified level set framework utilizing parameter priors for medical image segmentation

Image segmentation plays an important role in many medical imaging systems, yet in complex circumstances it remains an open problem. One of the main difficulties is the intensity inhomogeneity in an image. In order to tackle this problem, we first introduce a region-based level set segmentation framework to unify the traditional global and local methods. We then propose two novel parameter priors, i.e., the local order regularization and interactive regularization, and then utilize them as the constraints of the objective energy function. The objective energy function is finally minimized via a level set evolution process to achieve image segmentation. Extensive experiments show that the proposed approach has gained significant improvements in both accuracy and efficiency over the state-of-the-art methods.     

Graph-based image segmentation using directional nearest neighbor graph

Graph-based image segmentation techniques generally represent the problem in terms of a graph. In this work, we present a novel graph, called the directional nearest neighbor graph. The construction principle of this graph is that each node corresponding to a pixel in the image is connected to a fixed number of nearest neighbors measured by color value and the connected neighbors are distributed in four directions. Compared with the classical grid graph and the nearest neighbor graph, our method can capture low-level texture information using a less-connected edge topology. To test the performance of the proposed method, a comparison with other graph-based methods is carried out on synthetic and real-world images. Results show an improved segmentation for texture objects as well as a lower computational load.     

Perceptual image quality mutual information assessment metric using of Gabor features

A good objective metric of image quality assessment （IQA） should be consistent with the subjective judgment of human beings. In this paper, a four-stage perceptual approach for full reference IQA is presented. In the first stage, the visual features are extracted by 2-D Gabor filter that has the excellent performance of modeling the receptive fields of simple cells in the primary visual cortex. Then in the second stage, the extracted features are post-processed by the divisive normalization transform to reflect the nonlinear mechanisms in human visual systems. In the third stage, mutual information between the visual features of the reference and distorted images is employed to measure the visual quality. And in the last pooling stage, the mutual information is converted to the final objective quality score. Experimental results show that the proposed metic has a high correlation with the subjective assessment and outperforms other state-of-the-art metrics.     

Higher-Order Smoothing： A Novel Semantic Smoothing Method for Text Classification

It is known that latent semantic indexing （LSI） takes advantage of implicit higher-order （or latent） structure in the association of terms and documents. Higher-order relations in LSI capture ＂latent semantics＂. These findings have inspired a novel Bayesian framework for classification named Higher-Order Naive Bayes （HONB）, which was introduced previously, that can explicitly make use of these higher-order relations. In this paper, we present a novel semantic smoothing method named Higher-Order Smoothing （HOS） for the Naive Bayes algorithm. HOS is built on a similar graph based data representation of the HONB which allows semantics in higher-order paths to be exploited. We take the concept one step further in HOS and exploit the relationships between instances of different classes. As a result, we move beyond not only instance boundaries, but also class boundaries to exploit the latent information in higher-order paths. This approach improves the parameter estimation when dealing with insufficient labeled data. Results of our extensive experiments demonstrate the value of HOS oi1 several benchmark datasets.     

基于多标签判别字典学习的图像自动标注

针对图像自动标注中底层视觉特征与高层语义之间的语义鸿沟问题,在传统字典学习的基础上,提出一种基于多标签判别字典学习的图像自动标注方法。首先,为每幅图像提取多种类型特征,将多种特征组合作为字典学习输入特征空间的输入信息;然后,设计一个标签一致性正则化项,将原始样本的标签信息融入到初始的输入特征数据中,结合标签一致性判别字典和标签一致性正则化项进行字典学习;最后,通过得到的字典和稀疏编码矩阵求解标签稀疏编向量,实现未知图像的语义标注。在Corel 5K数据集上测试其标注性能,所提标注方法平均查准率和平均查全率分别可达到35%和48%;与传统的稀疏编码方法（MSC）相比,分别提高了10个百分点和16个百分点;与距离约束稀疏/组稀疏编码方法（DCSC/DCGSC）相比,分别提高了3个百分点和14个百分点。实验结果表明,所提方法能够较好地预测未知图像的语义信息,与当前几种流行的图像标注方法进行比较,所提方法具有较好的标注性能。     

Variational PCA fusion for resolution Pan-sharpening very high imagery

The Pan-sharpening approach based on principle component analysis （PCA） is affected by severe spectral distortion. To address this problem, a new pan-sharpening model based on PCA and variational technique is proposed to construct the substitute image of the first principal component （PC1）. The energy functional consists of three terms. The first term injects PC1 with the geometric structure of the panchromatic （Pan） image. The second term preserves the spectral pattern of the multi-spectral image in the merged result. And the third term guarantees the smoothness of the functional optimization solution. The fusion result is given by the minimum of the energy functional, which is computed with the gradient descend flow. The experiments on QuickBird and IKONOS datasets validate the effectiveness of the proposed model. Compared with the state- of-the-art pan-sharpening approaches, this model exhibits a better trade-off between improving spatial quality and preserving spectral signature of the MS image.     

Novel infrared and visible image fusion method based on independent component analysis

The goal of infrared （IR） and visible image fu- sion is for the fused image to contain IR object features from the IR image and retain the visual details provided by the visible image. The disadvantage of traditional fusion method based on independent component analysis （ICA） is that the primary feature information that describes the IR objects and the secondary feature information in the IR image are fused into the fused image. Secondary feature information can de- press the visual effect of the fused image. A novel ICA-based IR and visible image fusion scheme is proposed in this paper. ICA is employed to extract features from the infrared image, and then the primary and secondary features are distinguished by the kurtosis information of the ICA base coefficients. The secondary features of the IR image are discarded during fu- sion. The fused image is obtained by fusing primary features into the visible image. Experimental results show that the pro- posed method can provide better perception effect.     

一种基于稀疏典型性相关分析的图像检索方法

图像语义检索的一个关键问题就是要找到图像底层特征与语义之间的关联,由于文本是表达语义的一种有效手段,因此提出通过研究文本与图像两种模态之间关系来构建反映两者间潜在语义关联的有效模型的思路,基于该模型,可使用自然语言形式(文本语句)来表达检索意图,最终检索到相关图像.该模型基于稀疏典型性相关分析(sparse canonical correlation analysis,简称sparse CCA),按照如下步骤训练得到:首先利用隐语义分析方法构造文本语义空间,然后以视觉词袋(bag of visual words)来表达文本所对应的图像,最后通过Sparse CCA算法找到一个语义相关空间,以实现文本语义与图像视觉单词间的映射.使用稀疏的相关性分析方法可以提高模型可解释性和保证检索结果稳定性.实验结果验证了Sparse CCA方法的有效性,同时也证实了所提出的图像语义检索方法的可行性.