Faster split-based feedback network for image super-resolution

Although most of the existing image super-resolution(SR)methods have achieved superior performance,contrastive learning for high-level tasks has not been fully utilized in the existing image SR methods based on deep learning.This work focuses on two well-known strategies developed for lightweight and robust SR,i.e.,contrastive learning and feedback mechanism,and proposes an integrated solution called a split-based feedback network(SPFBN).The proposed SPFBN is based on a feedback mechanism to learn abstract representations and uses contrastive learning to explore high information in the representation space.Specifically,this work first uses hidden states and constraints in recurrent neural network(RNN)to implement a feedback mechanism.Then,use contrastive learning to perform representation learning to obtain high-level information by pushing the final image to the intermediate images and pulling the final SR image to the high-resolution image.Besides,a split-based feedback block(SPFB)is proposed to reduce model redundancy,which tolerates features with similar patterns but requires fewer parameters.Extensive experimental results demonstrate the superiority of the proposed method in comparison with the state-of-the-art methods.Moreover,this work extends the experiment to prove the effectiveness of this method and shows better overall reconstruction quality.

Shear Let Transform Residual Learning Approach for Single-Image Super-Resolution

Super-resolution techniques are employed to enhance image resolution by reconstructing high-resolution images from one or more low-resolution inputs.Super-resolution is of paramount importance in the context of remote sensing,satellite,aerial,security and surveillance imaging.Super-resolution remote sensing imagery is essential for surveillance and security purposes,enabling authorities to monitor remote or sensitive areas with greater clarity.This study introduces a single-image super-resolution approach for remote sensing images,utilizing deep shearlet residual learning in the shearlet transform domain,and incorporating the Enhanced Deep Super-Resolution network(EDSR).Unlike conventional approaches that estimate residuals between high and low-resolution images,the proposed approach calculates the shearlet coefficients for the desired high-resolution image using the provided low-resolution image instead of estimating a residual image between the high-and low-resolution image.The shearlet transform is chosen for its excellent sparse approximation capabilities.Initially,remote sensing images are transformed into the shearlet domain,which divides the input image into low and high frequencies.The shearlet coefficients are fed into the EDSR network.The high-resolution image is subsequently reconstructed using the inverse shearlet transform.The incorporation of the EDSR network enhances training stability,leading to improved generated images.The experimental results from the Deep Shearlet Residual Learning approach demonstrate its superior performance in remote sensing image recovery,effectively restoring both global topology and local edge detail information,thereby enhancing image quality.Compared to other networks,our proposed approach outperforms the state-of-the-art in terms of image quality,achieving an average peak signal-to-noise ratio of 35 and a structural similarity index measure of approximately 0.9.

AFBNet: A Lightweight Adaptive Feature Fusion Module for Super-Resolution Algorithms

At present,super-resolution algorithms are employed to tackle the challenge of low image resolution,but it is difficult to extract differentiated feature details based on various inputs,resulting in poor generalization ability.Given this situation,this study first analyzes the features of some feature extraction modules of the current super-resolution algorithm and then proposes an adaptive feature fusion block(AFB)for feature extraction.This module mainly comprises dynamic convolution,attention mechanism,and pixel-based gating mechanism.Combined with dynamic convolution with scale information,the network can extract more differentiated feature information.The introduction of a channel spatial attention mechanism combined with multi-feature fusion further enables the network to retain more important feature information.Dynamic convolution and pixel-based gating mechanisms enhance the module’s adaptability.Finally,a comparative experiment of a super-resolution algorithm based on the AFB module is designed to substantiate the efficiency of the AFB module.The results revealed that the network combined with the AFB module has stronger generalization ability and expression ability.

PSMFNet:Lightweight Partial Separation and Multiscale Fusion Network for Image Super-Resolution

The employment of deep convolutional neural networks has recently contributed to significant progress in single image super-resolution(SISR)research.However,the high computational demands of most SR techniques hinder their applicability to edge devices,despite their satisfactory reconstruction performance.These methods commonly use standard convolutions,which increase the convolutional operation cost of the model.In this paper,a lightweight Partial Separation and Multiscale Fusion Network(PSMFNet)is proposed to alleviate this problem.Specifically,this paper introduces partial convolution(PConv),which reduces the redundant convolution operations throughout the model by separating some of the features of an image while retaining features useful for image reconstruction.Additionally,it is worth noting that the existing methods have not fully utilized the rich feature information,leading to information loss,which reduces the ability to learn feature representations.Inspired by self-attention,this paper develops a multiscale feature fusion block(MFFB),which can better utilize the non-local features of an image.MFFB can learn long-range dependencies from the spatial dimension and extract features from the channel dimension,thereby obtaining more comprehensive and rich feature information.As the role of the MFFB is to capture rich global features,this paper further introduces an efficient inverted residual block(EIRB)to supplement the local feature extraction ability of PSMFNet.A comprehensive analysis of the experimental results shows that PSMFNet maintains a better performance with fewer parameters than the state-of-the-art models.

Efficient 2-D MUSIC algorithm for super-resolution moving target tracking based on an FMCW radar

Frequency modulated continuous wave(FMCW)radar is an advantageous sensor scheme for target estimation and environmental perception.However,existing algorithms based on discrete Fourier transform(DFT),multiple signal classification(MUSIC)and compressed sensing,etc.,cannot achieve both low complexity and high resolution simultaneously.This paper proposes an efficient 2-D MUSIC algorithm for super-resolution target estimation/tracking based on FMCW radar.Firstly,we enhance the efficiency of 2-D MUSIC azimuth-range spectrum estimation by incorporating 2-D DFT and multi-level resolution searching strategy.Secondly,we apply the gradient descent method to tightly integrate the spatial continuity of object motion into spectrum estimation when processing multi-epoch radar data,which improves the efficiency of continuous target tracking.These two approaches have improved the algorithm efficiency by nearly 2-4 orders of magnitude without losing accuracy and resolution.Simulation experiments are conducted to validate the effectiveness of the algorithm in both single-epoch estimation and multi-epoch tracking scenarios.

SR9009联合吲哚丙酸通过核因子κB信号通路减轻C2C12成肌细胞的炎症反应

背景:钟基因Rev-erbα参与调节炎症,但激活Rev-erbα会增加心脑血管疾病风险。为降低相关风险,探索Rev-erbα激动剂SR9009联合其他药物来减轻骨骼肌成肌细胞炎症,奠定治疗炎症相关性骨骼肌萎缩的理论基础。目的:探讨脂多糖刺激C2C12成肌细胞时吲哚丙酸、SR9009与核因子κB信号通路的关系。方法:①1μg/mL脂多糖刺激C2C12成肌细胞,RNA转录组测序结合KEGG通路富集分析信号通路。②CCK-8法检测C2C12成肌细胞活性,筛选吲哚丙酸的最佳给药浓度;然后将细胞分为空白对照组、脂多糖(1μg/mL)组、SR9009(10μmol/L)+脂多糖组、吲哚丙酸(80μmol/L)+脂多糖组、吲哚丙酸+SR9009+脂多糖组,ELISA检测细胞上清液中白细胞介素6水平,RT-qPCR检测白细胞介素6、肿瘤坏死因子α、Toll样受体4、CD14 mRNA表达,Western blot检测NF-κB p65、p-NF-κB p65蛋白表达。③siRNA敲减Rev-erbα,RT-qPCR评估敲减效率,检测白细胞介素6、肿瘤坏死因子αmRNA表达。结果与结论:①与空白对照组比较,脂多糖时间依赖性抑制成肌细胞融合形成肌管,白细胞介素6、肿瘤坏死因子αmRNA表达水平升高,细胞上清液中白细胞介素6水平显著升高;KEGG通路分析支持脂多糖刺激激活核因子κB信号通路。②吲哚丙酸浓度>80μmol/L时抑制C2C12成肌细胞活性;吲哚丙酸和SR9009通过抑制核因子κB信号通路发挥抗炎作用,降低白细胞介素6、肿瘤坏死因子α、Toll样受体4、CD14 mRNA表达水平,p-NF-κB p65/NF-κB p65蛋白表达比值低于脂多糖组。SR9009联合吲哚丙酸显著降低脂多糖诱导的炎症,Toll样受体4、CD14、白细胞介素6和肿瘤坏死因子αmRNA表达水平进一步下调,p-NF-κB p65/NF-κB p65蛋白表达比值显著低于吲哚丙酸+脂多糖组和SR9009+脂多糖组。③Rev-erbα随脂多糖刺激时间依赖性升高;siRNA敲减Rev-erbα效率达58%以上,成功敲减Rev-erbα后添加脂多糖,白细胞介素6和肿瘤坏死因子αmRNA表达较脂多糖组显著上调。④结果说明,Rev-erbα可以作为调节炎症反应的靶点,SR9009靶向激活Rev-erbα联合吲哚丙酸能抑制核因子κB信号通路显著减轻C2C12成肌细胞的炎症反应,联合抗炎效果优于单独干预。

Pyramid Separable Channel Attention Network for Single Image Super-Resolution

Single Image Super-Resolution(SISR)technology aims to reconstruct a clear,high-resolution image with more information from an input low-resolution image that is blurry and contains less information.This technology has significant research value and is widely used in fields such as medical imaging,satellite image processing,and security surveillance.Despite significant progress in existing research,challenges remain in reconstructing clear and complex texture details,with issues such as edge blurring and artifacts still present.The visual perception effect still needs further enhancement.Therefore,this study proposes a Pyramid Separable Channel Attention Network(PSCAN)for the SISR task.Thismethod designs a convolutional backbone network composed of Pyramid Separable Channel Attention blocks to effectively extract and fuse multi-scale features.This expands the model’s receptive field,reduces resolution loss,and enhances the model’s ability to reconstruct texture details.Additionally,an innovative artifact loss function is designed to better distinguish between artifacts and real edge details,reducing artifacts in the reconstructed images.We conducted comprehensive ablation and comparative experiments on the Arabidopsis root image dataset and several public datasets.The experimental results show that the proposed PSCAN method achieves the best-known performance in both subjective visual effects and objective evaluation metrics,with improvements of 0.84 in Peak Signal-to-Noise Ratio(PSNR)and 0.017 in Structural Similarity Index(SSIM).This demonstrates that the method can effectively preserve high-frequency texture details,reduce artifacts,and have good generalization performance.

Multi-prior physics-enhanced neural network enables pixel super-resolution and twin-imagefree phase retrieval from single-shot hologram

Digital in-line holographic microscopy(DIHM)is a widely used interference technique for real-time reconstruction of living cells’morphological information with large space-bandwidth product and compact setup.However,the need for a larger pixel size of detector to improve imaging photosensitivity,field-of-view,and signal-to-noise ratio often leads to the loss of sub-pixel information and limited pixel resolution.Additionally,the twin-image appearing in the reconstruction severely degrades the quality of the reconstructed image.The deep learning(DL)approach has emerged as a powerful tool for phase retrieval in DIHM,effectively addressing these challenges.However,most DL-based strategies are datadriven or end-to-end net approaches,suffering from excessive data dependency and limited generalization ability.Herein,a novel multi-prior physics-enhanced neural network with pixel super-resolution(MPPN-PSR)for phase retrieval of DIHM is proposed.It encapsulates the physical model prior,sparsity prior and deep image prior in an untrained deep neural network.The effectiveness and feasibility of MPPN-PSR are demonstrated by comparing it with other traditional and learning-based phase retrieval methods.With the capabilities of pixel super-resolution,twin-image elimination and high-throughput jointly from a single-shot intensity measurement,the proposed DIHM approach is expected to be widely adopted in biomedical workflow and industrial measurement.

Face image super-resolution reconstruction algorithm based on residual attention mechanism

Aiming at the problems such as low reconstruction efficiency,fuzzy texture details,and difficult convergence of reconstruction network face image super-resolution reconstruction algorithms,a new super-resolution reconstruction algorithm with residual concern was proposed.Firstly,to solve the influence of redundant and invalid information about the face image super-resolution reconstruction network,an attention mechanism was introduced into the feature extraction module of the network,which improved the feature utilization rate of the overall network.Secondly,to alleviate the problem of gradient disappearance,the adaptive residual was introduced into the network to make the network model easier to converge during training,and features were supplemented according to the needs during training.The experimental results showed that the proposed algorithm had better reconstruction performance,more facial details,and clearer texture in the reconstructed face image than the comparison algorithm.In objective evaluation,the proposed algorithm's peak signalto-noise ratio and structural similarity were also better than other algorithms.

湘中紫云山花岗岩和暗色包体的源区及其地质意义:来自全岩地球化学及Sr-Nd同位素的证据

湘中紫云山花岗质岩体形成于晚三叠世,近年来因其周边众多金属矿床的发现而备受关注。岩体内含有丰富的暗色包体(MMEs),为研究其源区和形成过程提供了一个重要的窗口。本文对紫云山暗色包体进行了全岩主、微量元素分析,并对紫云山花岗岩和暗色包体进行了Sr-Nd同位素分析。暗色包体主要为闪长质-花岗闪长质,SiO_(2)含量在55.24%~68.72%(平均为63.758%),K_(2)O含量为1.22%~6.33%(平均值为2.76%),属于钙碱性系列至高钾钙碱性系列岩石。暗色包体的(^(87)Sr/^(86)Sr)i值为0.714943~0.720623,ε_(Nd)(t)值为-9.53~-6.5,t DM2为1.53~1.78 Ga;主体花岗岩的(87 Sr/86 Sr)i值为0.718160~0.724384,ε_(Nd)(t)值为-7.4~-8.2,二阶段模式年龄t DM2为1.60~1.67Ga;补体花岗岩的(87 Sr/86 Sr)i值为0.735888~0.745734,ε_(Nd)(t)值为-10.1~-10.9,二阶段模式年龄t_(DM2)值为1.82~1.89Ga。研究表明:紫云山花岗岩主要来源于变杂砂岩熔融的壳源岩浆,并混合了部分富集幔源岩浆;其中主体花岗岩相较补体花岗岩含有较多的幔源组分,而紫云山暗色包体主要来源于富集地幔。紫云山花岗岩和暗色包体可能是印支期华南板块受周缘板块碰撞挤压后岩石圈伸展减薄,底侵的高温幔源岩浆注入到壳源长英质岩浆房后,两者混合不均的产物。在湘中地区,印支晚期花岗岩与其附近的金(锑)、钨等矿床存在密切的时、空联系,可能具有良好的成矿潜力;该区印支晚期花岗岩的成岩-成矿作用的强度远超传统认识,在找矿过程中需加以重视。

Learning Epipolar Line Window Attention for Stereo Image Super-Resolution Reconstruction

Transformer-based stereo image super-resolution reconstruction(Stereo SR)methods have significantly improved image quality.However,existing methods have deficiencies in paying attention to detailed features and do not consider the offset of pixels along the epipolar lines in complementary views when integrating stereo information.To address these challenges,this paper introduces a novel epipolar line window attention stereo image super-resolution network(EWASSR).For detail feature restoration,we design a feature extractor based on Transformer and convolutional neural network(CNN),which consists of(shifted)window-based self-attention((S)W-MSA)and feature distillation and enhancement blocks(FDEB).This combination effectively solves the problem of global image perception and local feature attention and captures more discriminative high-frequency features of the image.Furthermore,to address the problem of offset of complementary pixels in stereo images,we propose an epipolar line window attention(EWA)mechanism,which divides windows along the epipolar direction to promote efficient matching of shifted pixels,even in pixel smooth areas.More accurate pixel matching can be achieved using adjacent pixels in the window as a reference.Extensive experiments demonstrate that our EWASSR can reconstruct more realistic detailed features.Comparative quantitative results show that in the experimental results of our EWASSR on the Middlebury and Flickr1024 data sets for 2×SR,compared with the recent network,the Peak signal-to-noise ratio(PSNR)increased by 0.37 dB and 0.34 dB,respectively.

A super-resolution reconstruction algorithm for mural images based on improved generative adversarial network

In order to solve the problem of the lack of ornamental value and research value of ancient mural paintings due to low resolution and fuzzy texture details,a super resolution(SR)method based on generative adduction network(GAN)was proposed.This method reconstructed the detail texture of mural image better.Firstly,in view of the insufficient utilization of shallow image features,information distillation blocks(IDB)were introduced to extract shallow image features and enhance the output results of the network behind.Secondly,residual dense blocks with residual scaling and feature fusion(RRDB-Fs)were used to extract deep image features,which removed the BN layer in the residual block that affected the quality of image generation,and improved the training speed of the network.Furthermore,local feature fusion and global feature fusion were applied in the generation network,and the features of different levels were merged together adaptively,so that the reconstructed image contained rich details.Finally,in calculating the perceptual loss,the brightness consistency between the reconstructed fresco and the original fresco was enhanced by using the features before activation,while avoiding artificial interference.The experimental results showed that the peak signal-to-noise ratio and structural similarity metrics were improved compared with other algorithms,with an improvement of 0.512 dB-3.016 dB in peak signal-to-noise ratio and 0.009-0.089 in structural similarity,and the proposed method had better visual effects.

3D-CNNHSR: A 3-Dimensional Convolutional Neural Network for Hyperspectral Super-Resolution

Hyperspectral images can easily discriminate different materials due to their fine spectral resolution.However,obtaining a hyperspectral image(HSI)with a high spatial resolution is still a challenge as we are limited by the high computing requirements.The spatial resolution of HSI can be enhanced by utilizing Deep Learning(DL)based Super-resolution(SR).A 3D-CNNHSR model is developed in the present investigation for 3D spatial super-resolution for HSI,without losing the spectral content.The 3DCNNHSR model was tested for the Hyperion HSI.The pre-processing of the HSI was done before applying the SR model so that the full advantage of hyperspectral data can be utilized with minimizing the errors.The key innovation of the present investigation is that it used 3D convolution as it simultaneously applies convolution in both the spatial and spectral dimensions and captures spatial-spectral features.By clustering contiguous spectral content together,a cube is formed and by convolving the cube with the 3D kernel a 3D convolution is realized.The 3D-CNNHSR model was compared with a 2D-CNN model,additionally,the assessment was based on higherresolution data from the Sentinel-2 satellite.Based on the evaluation metrics it was observed that the 3D-CNNHSR model yields better results for the SR of HSI with efficient computational speed,which is significantly less than previous studies.

RealFuVSR:Feature enhanced real-world video super-resolution

Background Recurrent recovery is a common method for video super-resolution(VSR)that models the correlation between frames via hidden states.However,the application of this structure in real-world scenarios can lead to unsatisfactory artifacts.We found that in real-world VSR training,the use of unknown and complex degradation can better simulate the degradation process in the real world.Methods Based on this,we propose the RealFuVSR model,which simulates real-world degradation and mitigates artifacts caused by the VSR.Specifically,we propose a multiscale feature extraction module(MSF)module that extracts and fuses features from multiple scales,thereby facilitating the elimination of hidden state artifacts.To improve the accuracy of the hidden state alignment information,RealFuVSR uses an advanced optical flow-guided deformable convolution.Moreover,a cascaded residual upsampling module was used to eliminate noise caused by the upsampling process.Results The experiment demonstrates that RealFuVSR model can not only recover high-quality videos but also outperforms the state-of-the-art RealBasicVSR and RealESRGAN models.

Super-Resolution Stress Imaging for Terahertz-Elastic Based on SRCNN

Limited by diffraction limit, low spatial resolution is one of the shortcomings of terahertz imaging. Low spatial resolution is also one of the reasons limiting the development of stress measurement using terahertz imaging. In this paper, the full-field stress measurement using Terahertz Time Domain Spectroscopy (THz-TDS) is combined with Super-Resolution Convolutional Neural Network (SRCNN) algorithm to obtain stress fields with high spatial resolution. A modulation model from a plane stress state to a THz-TDS signal is constructed. A large number of simulated sets are obtained to train the SRCNN model. By applying the trained SRCNN model to imaging the numerical and physical stress fields, the improved spatial resolution of stress field calculated from the captured THz-TDS signal is obtained.

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.

Super-Resolution Image Reconstruction Based on an Improved Maximum a Posteriori Algorithm

A maximum a posteriori（ MAP） algorithm is proposed to improve the accuracy of super resolution（ SR） reconstruction in traditional methods. The algorithm applies both joints image registration and SR reconstruction in the framework,but separates them in the process of iteratiion. Firstly,we estimate the shifting parameters through two lowresolution（ LR） images and use the parameters to reconstruct initial HR images. Then,we update the shifting parameters using HR images. The aforementioned steps are repeated until the ideal HR images are obtained. The metrics such as PSNR and SSIM are used to fully evaluate the quality of the reconstructed image. Experimental results indicate that the proposed method can enhance image resolution efficiently.

东昆仑大格勒地区碱性杂岩体中辉石岩的年代学、地球化学、Sr-Nd同位素特征及其地质意义

近年团队在东昆仑大格勒地区发现了Nb、P矿化碱性杂岩体,并对杂岩体的岩石组合、岩石学特征及含矿性开展了研究,初步圈定了Nb、P矿化体,显示东昆仑具有寻找与碱性岩-碳酸岩型稀有稀土矿潜力。对碱性岩的形成时代、地球化学组成、矿物成分以及形成环境等方面的研究,不仅对重建东昆仑古构造环境具有重要的意义,还可推动东昆仑稀有金属成矿规律研究与找矿突破。本文在野外地质调查的基础上,选取杂岩体中重要的含矿辉石岩为研究对象,开展了单矿物电子探针原位分析、磷灰石和榍石U-Pb年代学、岩石地球化学及Sr-Nd同位素研究。结果显示,东昆仑大格勒地区辉石岩的形成时代为418 Ma。辉石岩主要矿物中存在似长石(霞石)、碱性暗色矿物(富铁黑云母),单斜辉石为透辉石,角闪石为钙质角闪石(铁韭闪石),黑云母为铁质黑云母和镁质黑云母。岩石地球化学特征显示辉石岩具有富K(K_(2)O>Na_(2)O)、CaO含量高、轻稀土元素强烈富集、富集Rb、Ba、Sr等大离子亲石元素,不亏损Nb、Ta元素,亏损Zr、U、Ti等高场强元素的特征。全岩的(87Sr/86Sr)i为0.704058~0.704278,ε_(Nd)(t)为-0.4~-0.2。矿物组成、元素和同位素地球化学特征均指示大格勒辉石岩为钾质碱性岩,具有与OIB相似的特征,岩浆源区为EMⅠ型地幔端元。岩石的形成过程为母岩浆在相对较深的地幔源区经历了1%~3%较低程度的部分熔融作用,在上侵过程中经历了较强的分离结晶作用和微弱的同化混染作用,其形成时代为该地区岩浆活动最强烈的时期,可能与碰撞后软流圈地幔的上涌和岩石圈的强烈伸展相关。

Sr含量对Al-Si合金显微组织和热导率的影响

以纯铝、Al-20Si和Al-10Sr中间合金为原料,Sr为变质剂(含量为0.01%、0.02%、0.04%和0.06%,质量分数),制备了Al-7Si-xSr、Al-12Si-xSr和Al-20Si-xSr合金,研究了Sr含量对Al-Si合金相变储热材料显微组织及热导率的影响。利用Hot Disk热常数分析仪测量合金的热导率,通过扫描电镜观察及分析合金的显微组织。结果表明:在Al-Si合金中添加变质元素Sr会影响合金的热导率,Al-7Si-0.04Sr合金热导率较Al-7Si合金增加了73.47 W·m^(-1)·K^(-1),Al-20Si-0.04Sr合金的热导率较Al-20Si合金增加了24.09 W·m^(-1)·K^(-1),Al-12Si-0.04Sr合金的热导率较Al-12Si合金增加了17.79 W·m^(-1)·K^(-1)。铝硅合金热导率的增长主要与α(Al)、共晶硅和初晶硅的形貌有关。经过Sr变质之后,Al-7Si合金中共晶Si立体形貌均由片层状转变为珊瑚状,Al-12Si和Al-20Si合金中共晶Si立体形貌由片层状转变为枝条状;其中,Al-7Si合金中α(Al)尺寸明显减少、排列紧密,二次枝晶臂间距逐渐减小;Al-20Si合金中的初晶Si尺寸明显减小,其形貌由多角的大块状变为小块状;α(Al)形态的转变不仅能够为自由电子的传输提供快速通道,而且还会使得共晶Si的排列更加规则,减少自由电子发生散射的几率,对合金的热导率影响较大。共晶Si由片层状转变为珊瑚状或枝条状,增加电子的平均自由程,有利于电子的传输。Al-20Si合金的热导率与初晶Si的形态有着重要联系,大尺寸且形状完整的初晶Si会发生晶格振动,会提高合金的热导率。

Super-resolution reconstruction for license plate images of moving vehicles

A novel reconstruction method to improve the recognition of license plate texts of moving vehicles in real traffic videos is proposed, which fuses complimentary information among low resolution （LR） images to yield a high resolution （HR） image. Based on the regularization super-resolution （SR） reconstruction schemes, this paper first introduces a residual gradient （RG） term as a new regularization term to improve the quality of the reconstructed image. Moreover, L1 norm is used to measure the residual data （RD） term and the RG term in order to improve the robustness of the proposed method. Finally, the steepest descent method is exploited to solve the energy functional. Simulated and real acquired video sequence experiments show the effectiveness and practicability of the proposed method and demonstrate its superiority over the bi-cubic interpolation and discontinuity adaptive Markov random field （DAMRF） SR method in both signal to noise ratios （SNR） and visual effects.