Undersampled MRI reconstruction with patch-based directional wavelets

Compressed sensing has shown great potential in reducing data acquisition time in magnetic resonance imaging (MRI). In traditional compressed sensing MRI methods, an image is reconstructed by enforcing its sparse representation with respect to a preconstructed basis or dictionary. In this paper, patch-based directional wavelets are proposed to reconstruct images from undersampled k-space data. A parameter of patch-based directional wavelets, indicating the geometric direction of each patch, is trained from the reconstructed image using conventional compressed sensing MRI methods and incorporated into the sparsifying transform to provide the sparse representation for the image to be reconstructed. A reconstruction formulation is proposed and solved via an efficient alternating direction algorithm. Simulation results on phantom and in vivo data indicate that the proposed method outperforms conventional compressed sensing MRI methods in preserving the edges and suppressing the noise. Besides, the proposed method is not sensitive to the initial image when training directions.     

A complex target terrain SAR raw data generation and evaluation based on inversed equalized hybrid-domain algorithm processing

In this paper, synthetic aperture radar raw data generation of complex target terrain based on inverse equalized hybrid-domain processing technique is proposed. Firstly, the basic synthetic aperture radar (SAR) echo model is derived via spectral analysis of extended chirp scaling. Then, the inverse equalized extended chirp scaling algorithm (ECSA) procedure is applied directly step by step to a real input SAR image in order to generate the raw data of the reference SAR image. The whole raw data generation (RDG) procedure only consists of Inverse Equalized ECSA (IEECSA) without integral equation and computation complexity, which means easier implementation and higher efficiency. By applying the resulted RDG into different image formation algorithms (IFAs), not only the final images are reconstructed but also the resulted RDG is evaluated in practice. Finally, valid image quality assessment techniques are implemented on the reconstructed images. The simulations not only confirm the validity of the proposed RDG method based on inverse equalized hybrid-domain technique but also evaluate the quality metrics of reconstructed images as a method of reliability assurance.     

Magnetic resonance image reconstruction using trained geometric directions in 2D redundant wavelets domain and non-convex optimization

Reducing scanning time is significantly important for MRI. Compressed sensing has shown promising results by undersampling the k-space data to speed up imaging. Sparsity of an image plays an important role in compressed sensing MRI to reduce the image artifacts. Recently, the method of patch-based directional wavelets (PBDW) which trains geometric directions from undersampled data has been proposed. It has better performance in preserving image edges than conventional sparsifying transforms. However, obvious artifacts are presented in the smooth region when the data are highly undersampled. In addition, the original PBDW-based method does not hold obvious improvement for radial and fully 2D random sampling patterns. In this paper, the PBDW-based MRI reconstruction is improved from two aspects: 1) An efficient non-convex minimization algorithm is modified to enhance image quality; 2) PBDW are extended into shift-invariant discrete wavelet domain to enhance the ability of transform on sparsifying piecewise smooth image features. Numerical simulation results on vivo magnetic resonance images demonstrate that the proposed method outperforms the original PBDW in terms of removing artifacts and preserving edges.     

Effective and robust infrared small target detection with the fusion of polydirectional first order derivative images under facet model

The robust detection of IR small target acts as one of the key techniques in the infrared search and tracking system (IRSTS). This paper presents a new method of small-target detection which formulates the problem as the detection of Gaussian-like spot. Initially, the amendatory first-order directional derivative (AFODD) based on facet model is applied to get the polydirectional derivative IR images, and the direction information of targets is reserved in these images. Then, the AFODD images are fused together to ensure the robustness and effectiveness of target detection. At last, the Principal Component Analysis (PCA) method is carried out to make targets in the fusion image more prominent, so that they can be extracted out by a simple threshold segmentation. Experiment results show that the presented method performs well even in the IR images with complex backgrounds.     

三维面形测量数据的计算全息可视化

提出利用计算机制全息进行三维面形测量数据立体重现的技术。首先利用三维面形测量技术同时获取三维物体的强度和距离像；然后根据三维面形测量数据，设计和制作菲涅耳计算全息图；最后将计算全息和光学全息相结合，以菲涅耳计算全息图的光学再现像为对象，记录光学像全息。这样既解决了计算机制全息术中真实三维物体立体信息数据捕捉的问题，又为三维面形检测提供了一个行之有效的立体重构技术。给出了这种方法的原理、计算全息的设计、制作方案和实验验证结果。     

Fingerprint image enhancement using multi-scale DDFB based diffusion filters and modified Hong filters

The main task of a fingerprint image enhancement is to enhance the image in such a way that it not only remove the noise but also enhance the reliable minutiae points. For this purpose, in this paper we propose a multi-scale decimation-free directional filter bank method for reliable orientation estimation. This reliable orientation is used in coherence enhancement diffusion and in Gabor filter based enhancement, which overcomes the drawbacks of these two methods. Experimental results show that the proposed method not only enhances the images but also facilitates the minutiae algorithm, by enhancing the true minutiae points.     

Technique for gray-scale visual light and infrared image fusion based on non-subsampled shearlet transform

A novel image fusion technique based on NSST (non-subsampled shearlet transform) is presented, aiming at resolving the fusion problem of spatially gray-scale visual light and infrared images. NSST, as a new member of MGA (multi-scale geometric analysis) tools, possesses not only flexible direction features and optimal shift-invariance, but much better fusion performance and lower computational costs compared with several current popular MGA tools such as NSCT (non-subsampled contourlet transform). We specifically propose new rules for the fusion of low and high frequency sub-band coefficients of source images in the second step of the NSST-based image fusion algorithm. First, the source images are decomposed into different scales and directions using NSST. Then, the model of region average energy (RAE) is proposed and adopted to fuse the low frequency sub-band coefficients of the gray-scale visual light and infrared images. Third, the model of local directional contrast (LDC) is given and utilized to fuse the corresponding high frequency sub-band coefficients. Finally, the final fused image is obtained by using inverse NSST to all fused sub-images. In order to verify the effectiveness of the proposed technique, several current popular ones are compared over three different publicly available image sets using four evaluation metrics, and the experimental results demonstrate that the proposed technique performs better in both subjective and objective qualities.     

基于支持度变换和top-hat分解的双色中波红外图像融合

为了解决用多尺度top-hat分解法融合双色中波红外图像时经常存在对比度提升有限、边缘区域失真较重的问题,提出了基于支持度变换和top-hat分解相结合的融合方法。先用支持度变换法将双色中波图像分解为低频图像和支持度图像序列;再从最后一层低频图像中用多尺度top-hat分解法提取各自的亮信息和暗信息;用灰度值取大法分别融合亮信息和暗信息;通过灰度值归一化和高斯滤波分别增强亮、暗信息融合图像;然后融合两低频图像和亮、暗信息增强图像;将融合图像作为新的低频图像和用灰度值取大法融合得到的支持度融合图像序列进行支持度逆变换,得到最终融合图像。该方法的实验结果同采用单一的支持度变换法融合和多尺度top-hat分解法融合相比,融合图像的对比度提升了11.69%,失真度降低了63.42%,局部粗糙度提高了38.12%。说明提出的从低频图像提取亮暗信息,并经过分别融合、增强,再与低频图像进行融合,能有效破解红外融合图像对比度提升和边缘区域失真度降低之间的矛盾,为提高图像融合质量提供了新方法。     

基于伪魏格纳分布分解的合成孔径雷达图像目标与阴影分割

在二维魏格纳分布的框架内,针对魏格纳变换的交叉项问题和计算量大的问题,提出了合成孔径雷达图像局部伪魏格纳变换的目标和目标阴影的分割方法.首先,将合成孔径雷达图像进行二维伪魏格纳变换,得到各像素点的二维能量谱图|然后提取各像素点的二维能量谱图对应位置值形成多个不同频段的与原图像同大小的能量谱图|最后,对不同频段的能量谱图采用不同的处理方法后,将各能量谱图相加处理后形成区域标识图像,最终得到原图像的目标和目标阴影分割图像.本文利用该方法对MSTAR切片图像进行了分割试验,并对分割图像与频谱最大值距离或方位分割算法和基于双参量CFAR与隐马尔科夫联合分割算法进行了分割图像对比度对比.实验结果表明,采用本文算法的合成孔径雷达分割图像,对比度明显提高,且保留了目标图像细节.     

An improved fusion algorithm for infrared and visible images based on multi-scale transform

In this paper, an improved fusion algorithm for infrared and visible images based on multi-scale transform is proposed. First of all, Morphology-Hat transform is used for an infrared image and a visible image separately. Then two images were decomposed into high-frequency and low-frequency images by contourlet transform (CT). The fusion strategy of high-frequency images is based on mean gradient and the fusion strategy of low-frequency images is based on Principal Component Analysis (PCA). Finally, the final fused image is obtained by using the inverse contourlet transform (ICT). The experiments and results demonstrate that the proposed method can significantly improve image fusion performance, accomplish notable target information and high contrast and preserve rich details information at the same time.     

Multiple-image hiding based on interference principle

A method for hiding multiple images into one image is presented. The method is based on interference principle and double random phase mask method. A uniform plane wave interferes with two beams of light wave carrying information of two encrypted images on an output plane. The obtained interference distribution image contains information of two encrypted images. By using frequency spectrum center shift technique, the two encrypted images can be recovered successfully. Then, the interference distribution is encoded into an index matrix through a host image. The optical encryption system parameters and the host image can all be used as encryption keys, which make encrypted image information safer. Numerical simulation indicates that the method can encrypt more information into one image and reconstruct the encryption image information successfully.     

基于四叉树和偏微分方程的灰度图像压缩

提出了一种基于四叉树和偏微分方程的灰度图像压缩方法。该方法只需传输和处理部分稀疏像素而不是整幅灰度图像。在编码端,首先采用四叉树分割图像,然后对部分像素进行编码传输。在解码端,采用基于偏微分方程的图像插值算法再生图像,有效地消除了解码图像中的方块效应。实验表明,该方法可以获得较高的压缩比和峰值信噪比,尤其是对尺寸较大且纹理细节较少的图像更是如此,并能较好地保持原始图像中灰度变化较大的细节,可以方便地去除方块效应,具有较高的实用价值。     

Biometric identification using local iterated function

Biometric identification protocol has been received an increasing interest recently. It is a process that determines person identity by making use of their biometric features. A new biometric identification method is presented in this paper based on partial self-similarity that used to identify features within fingerprint images. This approach is already used in Fractal Image Compression (FIC) due to their ability to represent the images by a limited number of affine transformations, and its variation of scale, translation or rotation. These features give the recognition process high impact and good performance. To process data in a fingerprint image, it first converted into digital format using Optical Fingerprint Reader (OFR). The verification process is done by comparing these data with the server data. The system analysis shows that the proposed method is efficient in terms of memory and time complexity.     

Detection of micro solder balls using active thermography and probabilistic neural network

Micro solder ball/bump has been widely used in electronic packaging. It has been challenging to inspect these structures as the solder balls/bumps are often embedded between the component and substrates, especially in flip-chip packaging. In this paper, a detection method for micro solder ball/bump based on the active thermography and the probabilistic neural network is investigated. A VH680 infrared imager is used to capture the thermal image of the test vehicle, SFA10 packages. The temperature curves are processed using moving average technique to remove the peak noise. And the principal component analysis (PCA) is adopted to reconstruct the thermal images. The missed solder balls can be recognized explicitly in the second principal component image. Probabilistic neural network (PNN) is then established to identify the defective bump intelligently. The hot spots corresponding to the solder balls are segmented from the PCA reconstructed image, and statistic parameters are calculated. To characterize the thermal properties of solder bump quantitatively, three representative features are selected and used as the input vector in PNN clustering. The results show that the actual outputs and the expected outputs are consistent in identification of the missed solder balls, and all the bumps were recognized accurately, which demonstrates the viability of the PNN in effective defect inspection in high-density microelectronic packaging.     

Image registration guided,sparsity constrained reconstructions for dynamic MRI

It is generally a challenging task to reconstruct dynamic magnetic resonance (MR) images with high spatial and high temporal resolutions, especially with highly incomplete k-space sampling. In this work, a novel method that combines a non-rigid image registration technique with sparsity-constrained image reconstruction is introduced. Employing a multi-resolution free-form deformation technique with B-spline interpolations, the non-rigid image registration accurately models the complex deformations of the physiological dynamics, and provides artifact-suppressed high spatial-resolution predictions. Based on these prediction images, the sparsity-constrained data fidelity-enforced image reconstruction further improves the reconstruction accuracy. When compared with the k-t FOCUSS with motion estimation/motion compensation (MEMC) technique on volunteer scans, the proposed method consistently outperforms in both the spatial and the temporal accuracy with variously accelerated k-space sampling. High fidelity reconstructions for dynamic systolic phases with reduction factor of 10 and cardiac perfusion series with reduction factor of 3 are presented.     

Active polarization imaging method for latent fingerprint detection

Fingerprint identification is one of the reliable methods for personal identification, and is becoming more and more widely used in society. In this paper, the polarization characteristic of latent fingerprint is studied, and the experimental platform for polarization imaging detection of latent fingerprint based on active light source is built. Through quality evaluation and comprehensive analysis of the fingerprint images, we obtain the best analytical method and the best angle for the detection of latent fingerprint polarization imaging. Using the difference and complementarity between different type of polarization images, a fusion algorithm of polarization image and intensity image is proposed. The results show that compared with the general intensity imaging, the latent fingerprint image obtained by polarization imaging method is clearer and the details are more abundant.     

Unified Generative Adversarial Networks for Multidomain Fingerprint Presentation Attack Detection

With the rapid growth of fingerprint-based biometric systems, it is essential to ensure the security and reliability of the deployed algorithms. Indeed, the security vulnerability of these systems has been widely recognized. Thus, it is critical to enhance the generalization ability of fingerprint presentation attack detection (PAD) cross-sensor and cross-material settings. In this work, we propose a novel solution for addressing the case of a single source domain (sensor) with large labeled real/fake fingerprint images and multiple target domains (sensors) with only few real images obtained from different sensors. Our aim is to build a model that leverages the limited sample issues in all target domains by transferring knowledge from the source domain. To this end, we train a unified generative adversarial network (UGAN) for multidomain conversion to learn several mappings between all domains. This allows us to generate additional synthetic images for the target domains from the source domain to reduce the distribution shift between fingerprint representations. Then, we train a scale compound network (EfficientNetV2) coupled with multiple head classifiers (one classifier for each domain) using the source domain and the translated images. The outputs of these classifiers are then aggregated using an additional fusion layer with learnable weights. In the experiments, we validate the proposed methodology on the public LivDet2015 dataset. The experimental results show that the proposed method improves the average classification accuracy over twelve classification scenarios from 67.80 to 80.44% after adaptation.     

Attack on double-random-phase-encoding-based image hiding method

The double-random phase-encoding (DRPE) technique is a typical optical image encryption technique, which can also be used for image hiding. Usually, the secret image is encrypted with the DRPE technique and the encoded image is hidden into the host image via superimposition to obtain the stego-image. The attack technique on the DRPE-based image hiding method was proposed in this paper. Firstly, a randomly selected superimposition coefficient was used to approximate the original superimposition coefficient to extract the hidden encoded images from the stego-images approximately. Then, the chosen-plaintext attack technique on the DRPE-based optical image encryption technique was applied to recover the random phase masks used in the DRPE technique. The theoretical analysis indicated that, without considering the computational error, the recovered secret image via the proposed attack technique is identical to the original one. Even considering the computational error, it is identical to the secret image recovered with the original DRPE-based image hiding method, which demonstrates that the attack on the DRPE-based image hiding method is successfully achieved. The numerical simulation results demonstrated the correctness of the theoretical analysis.     

双重分数傅里叶变换计算全息

提出了双重分数傅里叶变换计算全息，在这种方法中，将两个图像的信息分别经不同阶的分数傅里叶变换后，记录在同一张分数傅里叶变换计算全息图上，它需要两个特定的分数傅里叶变换系统才能再现出所记录的图像信息，利用其再现方式的特殊性，可制成一种新的安全认证系统。     

A new approach for colored watermarking image into gray scale image using wavelet fusion

With the quick growth of technologies and great spread of the Internet, many challenges face data hiding. Nevertheless, significant data may be protected by burring it in an image as a watermark. This paper shows an efficient data hiding watermarking approach for color image by using singular value decomposition (SVD), multi-level discrete wavelet transform (DWT) and wavelet fusion. The main idea in this work is to separate the color image into its basic components (three channels); red, green, and blue. Then, fuse every channel with an image which is gray scale and integrate the three fused images into one gray scale fused image. Finally, the fused image is burred into a cover gray scale image to produce the watermarked image by using DWT and SVD. The proposed approach evaluation is done by using several images and different hacking on the transmitted image. The experimental results show that the marked images which are generated by the proposed approach are tolerant to versatile attacks such as Gaussian, blur, wrap, and cropping. Above all, the extracted watermark images are recognized even when the watermarked images suffered from attacks.