Multi-Modal Medical Image Fusion Based on Improved Parameter Adaptive PCNN and Latent Low-Rank Representation

Multimodal medical image fusion can help physicians provide more accurate treatment plans for patients, as unimodal images provide limited valid information. To address the insufficient ability of traditional medical image fusion solutions to protect image details and significant information, a new multimodality medical image fusion method(NSST-PAPCNNLatLRR) is proposed in this paper. Firstly, the high and low-frequency sub-band coefficients are obtained by decomposing the source image using NSST. Then, the latent low-rank representation algorithm is used to process the low-frequency sub-band coefficients;An improved PAPCNN algorithm is also proposed for the fusion of high-frequency sub-band coefficients. The improved PAPCNN model was based on the automatic setting of the parameters, and the optimal method was configured for the time decay factor αe. The experimental results show that, in comparison with the five mainstream fusion algorithms, the new algorithm has significantly improved the visual effect over the comparison algorithm,enhanced the ability to characterize important information in images, and further improved the ability to protect the detailed information;the new algorithm has achieved at least four firsts in six objective indexes.

Multimodal Medical Image Fusion Based on Parameter Adaptive PCNN and Latent Low-rank Representation

Medical image fusion has been developed as an efficient assistive technology in various clinical applications such as medical diagnosis and treatment planning.Aiming at the problem of insufficient protection of image contour and detail information by traditional image fusion methods,a new multimodal medical image fusion method is proposed.This method first uses non-subsampled shearlet transform to decompose the source image to obtain high and low frequency subband coefficients,then uses the latent low rank representation algorithm to fuse the low frequency subband coefficients,and applies the improved PAPCNN algorithm to fuse the high frequency subband coefficients.Finally,based on the automatic setting of parameters,the optimization method configuration of the time decay factorαe is carried out.The experimental results show that the proposed method solves the problems of difficult parameter setting and insufficient detail protection ability in traditional PCNN algorithm fusion images,and at the same time,it has achieved great improvement in visual quality and objective evaluation indicators.

Low-Rank and Sparse Representation with Adaptive Neighborhood Regularization for Hyperspectral Image Classification

Low-Rank and Sparse Representation(LRSR)method has gained popularity in Hyperspectral Image(HSI)processing.However,existing LRSR models rarely exploited spectral-spatial classification of HSI.In this paper,we proposed a novel Low-Rank and Sparse Representation with Adaptive Neighborhood Regularization(LRSR-ANR)method for HSI classification.In the proposed method,we first represent the hyperspectral data via LRSR since it combines both sparsity and low-rankness to maintain global and local data structures simultaneously.The LRSR is optimized by using a mixed Gauss-Seidel and Jacobian Alternating Direction Method of Multipliers(M-ADMM),which converges faster than ADMM.Then to incorporate the spatial information,an ANR scheme is designed by combining Euclidean and Cosine distance metrics to reduce the mixed pixels within a neighborhood.Lastly,the predicted labels are determined by jointly considering the homogeneous pixels in the classification rule of the minimum reconstruction error.Experimental results based on three popular hyperspectral images demonstrate that the proposed method outperforms other related methods in terms of classification accuracy and generalization performance.

Robust Principal Component Analysis Integrating Sparse and Low-Rank Priors

Principal Component Analysis (PCA) is a widely used technique for data analysis and dimensionality reduction, but its sensitivity to feature scale and outliers limits its applicability. Robust Principal Component Analysis (RPCA) addresses these limitations by decomposing data into a low-rank matrix capturing the underlying structure and a sparse matrix identifying outliers, enhancing robustness against noise and outliers. This paper introduces a novel RPCA variant, Robust PCA Integrating Sparse and Low-rank Priors (RPCA-SL). Each prior targets a specific aspect of the data’s underlying structure and their combination allows for a more nuanced and accurate separation of the main data components from outliers and noise. Then RPCA-SL is solved by employing a proximal gradient algorithm for improved anomaly detection and data decomposition. Experimental results on simulation and real data demonstrate significant advancements.

Weighted Sparse Image Classification Based on Low Rank Representation

The conventional sparse representation-based image classification usually codes the samples independently,which will ignore the correlation information existed in the data.Hence,if we can explore the correlation information hidden in the data,the classification result will be improved significantly.To this end,in this paper,a novel weighted supervised spare coding method is proposed to address the image classification problem.The proposed method firstly explores the structural information sufficiently hidden in the data based on the low rank representation.And then,it introduced the extracted structural information to a novel weighted sparse representation model to code the samples in a supervised way.Experimental results show that the proposed method is superiority to many conventional image classification methods.

Improved Weight Function for Nonlocal Means Image Denoising

The nonlocal means( NLM) has been widely used in image processing. In this paper,we introduce a modified weight function for NLM denoising, which will compute the nonlocal similarities among the pre-processing pixel patches instead of the commonly used similarity measure based on noisy observations. By the law of large number,the norm for the pre-processing pixel patches is closer to the norm of the original clean pixel patches,so the proposed weight functions are more optimized and the selected similar patches are more accurate. Experimental results indicate the proposed algorithm achieves better restored results compared to the classical NLM's method.

Fast image super-resolution algorithm based on multi-resolution dictionary learning and sparse representation

Sparse representation has attracted extensive attention and performed well on image super-resolution（SR） in the last decade. However, many current image SR methods face the contradiction of detail recovery and artifact suppression. We propose a multi-resolution dictionary learning（MRDL） model to solve this contradiction, and give a fast single image SR method based on the MRDL model. To obtain the MRDL model, we first extract multi-scale patches by using our proposed adaptive patch partition method（APPM）. The APPM divides images into patches of different sizes according to their detail richness. Then, the multiresolution dictionary pairs, which contain structural primitives of various resolutions, can be trained from these multi-scale patches.Owing to the MRDL strategy, our SR algorithm not only recovers details well, with less jag and noise, but also significantly improves the computational efficiency. Experimental results validate that our algorithm performs better than other SR methods in evaluation metrics and visual perception.

Image super-resolution reconstruction based on sparse representation and residual compensation

A super-resolution reconstruction algorithm is proposed. The algorithm is based on the idea of the sparse representation of signals, by using the fact that the sparsest representation of a sig- nal is unique as the constraint of the patched-based reconstruction, and compensating residual errors of the reconstruction results both locally and globally to solve the distortion problem in patch-based reconstruction algorithms. Three reconstruction algorithms are compared. The results show that the images reconstructed with the new algorithm have the best quality.

Digital image inpainting by example-based image synthesis method

A simple and effective image inpainting method is proposed in this paper, which is proved to be suitable for different kinds of target regions with shapes from little scraps to large unseemly objects in a wide range of images. It is an important improvement upon the traditional image inpainting techniques. By introducing a new bijeetive-mapping term into the matching cost function, the artificial repetition problem in the final inpainting image is practically solved. In addition, by adopting an inpainting error map, not only the target pixels are refined gradually during the inpainting process but also the overlapped target patches are combined more seamlessly than previous method. Finally, the inpainting time is dramatically decreased by using a new acceleration method in the matching process.

Brief review of image denoising techniques

With the explosion in the number of digital images taken every day,the demand for more accurate and visually pleasing images is increasing.However,the images captured by modern cameras are inevitably degraded by noise,which leads to deteriorated visual image quality.Therefore,work is required to reduce noise without losing image features(edges,corners,and other sharp structures).So far,researchers have already proposed various methods for decreasing noise.Each method has its own advantages and disadvantages.In this paper,we summarize some important research in the field of image denoising.First,we give the formulation of the image denoising problem,and then we present several image denoising techniques.In addition,we discuss the characteristics of these techniques.Finally,we provide several promising directions for future research.

Generalized Nonconvex Low-Rank Algorithm for Magnetic Resonance Imaging (MRI) Reconstruction

In recent years,utilizing the low-rank prior information to construct a signal from a small amount of measures has attracted much attention.In this paper,a generalized nonconvex low-rank(GNLR) algorithm for magnetic resonance imaging(MRI)reconstruction is proposed,which reconstructs the image from highly under-sampled k-space data.In the algorithm,the nonconvex surrogate function replacing the conventional nuclear norm is utilized to enhance the low-rank property inherent in the reconstructed image.An alternative direction multiplier method(ADMM) is applied to solving the resulting non-convex model.Extensive experimental results have demonstrated that the proposed method can consistently recover MRIs efficiently,and outperforms the current state-of-the-art approaches in terms of higher peak signal-to-noise ratio(PSNR) and lower high-frequency error norm(HFEN) values.

SRResNet Performance Enhancement Using Patch Inputs and Partial Convolution-Based Padding

Due to highly underdetermined nature of Single Image Super-Resolution(SISR)problem,deep learning neural networks are required to be more deeper to solve the problem effectively.One of deep neural networks successful in the Super-Resolution(SR)problem is ResNet which can render the capability of deeper networks with the help of skip connections.However,zero padding(ZP)scheme in the network restricts benefits of skip connections in SRResNet and its performance as the ratio of the number of pure input data to that of zero padded data increases.In this paper.we consider the ResNet with Partial Convolution based Padding(PCP)instead of ZP to solve SR problem.Since training of deep neural networks using patch images is advantageous in many aspects such as the number of training image data and network complexities,patch image based SR performance is compared with single full image based one.The experimental results show that patch based SRResNet SR results are better than single full image based ones and the performance of deep SRResNet with PCP is better than the one with ZP.

MR IMAGE RECONSTRUCTION BASED ON COMPREHENSIVE SPARSE PRIOR

In this paper, a novel Magnetic Resonance (MR) reconstruction framework which combines image-wise and patch-wise sparse prior is proposed. For addressing, a truncated beta-Bernoulli process is firstly employed to enforce sparsity on overlapping image patches emphasizing local structures. Due to its properties, beta-Bernoulli process can adaptive infer the sparsity (number of non-zero coefficients) of each patch, an appropriate dictionary, and the noise variance simultaneously, which are prerequisite for iterative image reconstruction. Secondly, a General Gaussian Distribution (GGD) prior is introduced to engage image-wise sparsity for wavelet coefficients, which can be then estimated by a threshold denoising algorithm. Finally, MR image is reconstructed by patch-wise estimation, image-wise estimation and under-sampled k-space data with least square data fitting. Experimental results have demonstrated that proposed approach exhibits excellent reconstruction performance. Moreover, if the image is full of similar low-dimensional-structures, proposed algorithm has dramatically improved Peak Signal to Noise Ratio (PSNR) 7～9 dB, with comparisons to other state-of-art compressive sampling methods.

Analysis and Design of Broadband Stacked Microstrip Patch Antennas

A broadband microstrip patch antenna was analyzed and designed.Full wave analysis method(FWAM) was employed to show that a stacked microstrip dual patch antenna(SMDPA) might have a much wider bandwidth than that of the ordinanry uni patch one.By means of discrete complex image theory(DCIT),the Sommerfeld integrals (SI) involved were accurately calculated at a speed several hundred times faster than numerical integration method(NIM).The feeding structure of the SMDPA was then improved and the bandwidth was extended to about 22% or more for voltage standing wave ratio (VSWR)s≤2 Finally,a matching network was constructed to obtain a bandwidth of about 25% for s≤1.5.

基于三维小波变换的高光谱图像分类算法

针对如何充分利用空间特征来达到较高的高光谱图像分类精度的问题,提出了一种基于三维离散小波变换(3D-DWT)与随机补丁网络(RPNet)结合的高光谱图像的地物属性分类算法。在分类过程中,综合3D-DWT提取的特征和RPNet深度学习框架提取的特征,利用支持向量机(SVM)对特征向量进行分类。所提出的方法在Indian Pines和University of Pavia两个数据集上进行测试,结果表明该方法比现有方法有显著的分类性能的提高。

融合视觉中心机制和并行补丁感知的遥感图像检测算法

针对遥感图像存在复杂背景干扰、目标多尺度差异和微小目标提取难的问题,本文基于YOLOv7-tiny模型提出一种融合视觉中心机制和并行补丁感知的遥感图像检测算法。该算法一是引入显式视觉中心机制,构建像素点间的长距离依赖关系,丰富图像的整体语义信息,同时提升对目标纹理的提取性能;二是改进并行补丁感知模块,调整特征提取感受野,以适应不同目标尺度;三是设计多尺度特征融合模块,实现对多层特征的高效融合,提升模型推理速度。在公共数据集RSOD上进行实验,所提算法的准确率、召回率和平均准确率均值相较YOLOv7-tiny分别提升1.5%、2.4%和2.4%,此外在NWPUVHR-10和DOTA数据集上进行泛化性验证,结果表明本文算法具备较强的泛化性能。通过与不同算法对比分析,进一步体现本文算法性能的优越性。

改扩建砖混隐患房屋机器视觉辨识模型

改扩建砖混房屋存量巨大,且易发生坍塌事故。仅靠人工方式辨识隐患房屋较为低效,尝试引入机器视觉模型更智能地辨识隐患房屋。首先,收集已标注安全现状的相关房屋图像7 114幅,经一定处理后按6∶2∶2的比例划分训练集、验证集与测试集;之后,选用ConvNeXt、Swin Transformer、ResNeXt、DenseNet和MobileNet五种视觉模型,以迁移学习的方法,进行适应本识别任务的微调训练,并与引入“随机裁剪拼接(Random Image Cropping and Patching, RICAP)”图像数据增强方法的训练结果对比;最后,综合运用多种指标评价各个模型表现。结果表明:引入RICAP方法训练的ConvNeXt模型,在测试集上取得0.945 9的准确率、0.974 2的召回率,比未使用该方法训练的表现最优的模型分别提升0.014 8、0.044 4,可更准确地辨识隐患房屋。

基于特征提取的遥感影像异常图斑识别方法

为发挥遥感影像更高的市场价值,实现对影像中异常图斑的精准识别,引进特征提取技术,开展遥感影像异常图斑识别方法的研究。遥感影像获取及预处理;引进特征提取技术对遥感影像进行关键区域分割;对分割后的遥感图像进行图斑纹理特征抽取,根据不同图斑的纹理特征变化,实现对图斑中异常信息的识别。将文献[1]、[3]方法作为参照,设计对比实验,实验结果证明:提出的基于特征提取的方法在应用中的效果良好,该方法可以精准辨识遥感影像中异常图斑,并实现对异常图斑的标注。

大规模景观图像斑块特征增强算法仿真

不同景观斑块特征存在一定的差异,整体增强会导致斑块重叠和模糊等问题。为此,提出一种大规模景观图像斑块特征增强算法。计算大规模景观图像斑块形状指数、多样性指数、破碎性指数、最大斑块指数以及优势度指数,以此反映景观图像内斑块组成和结构特征,并度量景观斑块特征;再将所有指数计算结果组成斑块特征集,输入多分支注意力机制卷积神经网络中,依据网络通道注意力机制增强图像斑块特征分辨率;最后,将增强结果作为局部特征融合网络的输入,通过该网络的卷积操作生成各个通道的局部斑块图,获取局部特征、斑块特征的位置和细节信息,完成斑块特征二次增强。仿真实验结果表明:所提出的增强算法的梯度损失和结构相似性损失函数值均在0.10以下,说明其能够有效处理斑块边缘之间的模糊效应,并且可靠区分不同的景观斑块分布空间。

Issues and controversies in esophageal inlet patch

The proximal esophagus is rarely examined,and its inspection is often inadequate.Optical chromoendoscopy techniques such as narrow band imaging improve the detection rate of inlet patches in the proximal esophagus,a region in which their prevalence is likely underestimated.Various studies have reported correlations between these esophageal marks with different issues such as Barrett’s esophagus,but these findings remain controversial.Conflicting reports complicate the process of interpreting the clinical features of esophageal inlet patches and underestimate their importance.Unfortunately,the limited clinical data and statistical analyses make reaching any conclusions difficult.It is hypothesized that inlet patches are correlated with various esophageal and extraesophageal symptoms,diagnoses and the personalized therapeutic management of patients with inlet patches as well as the differential diagnosis for premalignant lesions or early cancers.Due to its potential underdiagnosis,there are no consensus guidelines for the management and follow up of inlet patches.This review focuses on questions that were raised from published literature on esophageal inlet patches in adults.