TranSR-Ne RF:Super-resolution neural radiance field for reconstruction and rendering of weak and repetitive texture of aviation damaged functional surface

In order to reconstruct and render the weak and repetitive texture of the damaged functional surface of aviation,an improved neural radiance field,named TranSR-NeRF,is proposed.In this paper,a data acquisition system was designed and built.The acquired images generated initial point clouds through TransMVSNet.Meanwhile,after extracting features from the images through the improved SE-ConvNeXt network,the extracted features were aligned and fused with the initial point cloud to generate high-quality neural point cloud.After ray-tracing and sampling of the neural point cloud,the ResMLP neural network designed in this paper was used to regress the volume density and radiance under a given viewing angle,which introduced spatial coordinate and relative positional encoding.The reconstruction and rendering of arbitrary-scale super-resolution of damaged functional surface is realized.In this paper,the influence of illumination conditions and background environment on the model performance is also studied through experiments,and the comparison and ablation experiments for the improved methods proposed in this paper is conducted.The experimental results show that the improved model has good effect.Finally,the application experiment of object detection task is carried out,and the experimental results show that the model has good practicability.

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.

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.

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.

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.

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.

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.

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。

东昆仑大格勒地区富铌橄榄岩矿物学、地球化学及Sr-Nd同位素特征

东昆仑大格勒地区碳酸岩-碱性杂岩富铌稀土矿化的发现,对青海省优质稀有稀土矿产的勘查及综合研究具有重要启示。本文以大格勒杂岩体中蛇纹石化富铌橄榄岩为研究对象,开展了矿物学、岩石地球化学和Sr-Nd同位素研究。矿物学分析显示,富铌橄榄岩中铌矿物主要为铌易解石(Nb_(2)O_(5)=50.33%~52.50%)、铌钙矿(Nb_(2)O_(5)=48.20%~50.27%)、褐铈铌矿(Nb_(2)O_(5)=42.80%~43.45%)、Stefanweissite矿(Nb_(2)O_(5)=33.14%~34.78%),次为钛铁矿(Nb_(2)O_(5)=3.45%~4.89%)。样品具低SiO_(2)(20.16%~29.20%)和K_(2)O+Na_(2)O(0.02%~2.47%)含量,高Mg O(22.88%~27.69%)含量和Mg^(#)值(74~82),稀土元素总量较高(∑REE=772~1807μg/g),稀土元素配分模式强烈富集LREE、亏损HREE,Eu异常不明显(δEu=0.87~1.17)。原始地幔标准化微量元素蛛网图显示,样品富集Nb、Ta、Th、U、Sr、Pb,Nb含量高,为981~1753μg/g,平均1378μg/g,可能与晚阶段碳酸岩侵入流体交代作用有关。样品的(87Sr/86Sr)i值为0.70378~0.70396,ε_(Nd)(t)值为-0.49~0.31,岩浆源区可能为EMⅠ型地幔端元。综合区域构造背景,大格勒岩体中富铌橄榄岩的形成可能与该地区拉张环境下岩石圈的强烈伸展和软流圈地幔的上涌相关。

超声处理对可降解Zn-0.5Sr合金组织及腐蚀性能的影响

以Zn-0.5Sr合金为对象,研究了不同超声功率(0、300、600、900 W)对Zn-0.5Sr合金显微组织及腐蚀性能的影响.结果表明:Zn-0.5Sr合金经过超声处理后,铸态组织中SrZn13相由粗大的多边形转变为细小的块状,与未处理的合金相比较,对合金进行600 W超声处理后,合金的电化学腐蚀速率由2.078±0.141 mm/a增加至5.747±0.390 mm/a.当超声功率为600 W时,Zn-0.5Sr合金15、30 d的浸泡腐蚀速度分别为0.090±0.0021、0.074±0.0019 mm/a,是未经超声处理的1.88、1.95倍.

四川甘洛铅锌矿集区闪锌矿Rb-Sr等时线年龄及其地质意义

四川甘洛铅锌矿集区位于扬子地块西南缘的川滇黔铅锌成矿带北段,是四川主要的铅锌产地,具有重要经济价值。为研究该矿集区铅锌成矿时代、成矿大地构造背景和成矿机制,以支撑区域找矿勘查,在区内选择赤普和尔呷地吉两个典型铅锌矿床开展闪锌矿Rb-Sr同位素体系研究,获得Rb-Sr等时线年龄246±17 Ma(MSWD=2.3),表明区内铅锌矿化作用发生于早三叠世,与古特提斯洋闭合时限吻合;闪锌矿(87Sr/86Sr)i值变化于0.71061~0.71393,高于幔源87Sr/86Sr值0.70355及峨眉山玄武岩87Sr/86Sr值0.704979~0.706938,低于基底岩石87Sr/86Sr值0.7243~0.7288,暗示成矿物质主要来源于地壳。综合前人研究,认为在古特提斯洋闭合背景下,强烈造山运动诱发盆地卤水深循环并萃取基底地层中的成矿物质,在峨眉山玄武岩岩浆活动的热动力条件下,含矿流体沿马拉哈断裂进一步迁移沉淀形成了赤普和尔呷地吉铅锌矿床。马拉哈深大断裂及造山作用派生的层间或断层破碎带、碳酸盐岩地层是甘洛地区主要的控矿要素。

基于深度SR模型的加密数字图像压缩与重构

针对图像压缩后降低存储空间的同时也降低图像分辨率的问题,提出一种基于深度超分辨率(SR)模型的加密数字图像压缩与重构方法。先对加密数字图像进行分割,然后针对分割后的图像子块进行编码压缩处理,并将经典的SR重构方法(稀疏编码法)与深度学习(卷积神经网络)进行结合,构建一种深度SR模型,并利用模型对图像进行压缩和解压,最后对解密后的数字图像进行重构。结果表明:图像压缩后较压缩前占据存储空间降低,压缩效果有所改善,经过深度SR模型重构后的数字图像分辨率相对更高,且峰值信噪比更高。

Contrastive Learning for Blind Super-Resolution via A Distortion-Specific Network

Previous deep learning-based super-resolution(SR)methods rely on the assumption that the degradation process is predefined(e.g.,bicubic downsampling).Thus,their performance would suffer from deterioration if the real degradation is not consistent with the assumption.To deal with real-world scenarios,existing blind SR methods are committed to estimating both the degradation and the super-resolved image with an extra loss or iterative scheme.However,degradation estimation that requires more computation would result in limited SR performance due to the accumulated estimation errors.In this paper,we propose a contrastive regularization built upon contrastive learning to exploit both the information of blurry images and clear images as negative and positive samples,respectively.Contrastive regularization ensures that the restored image is pulled closer to the clear image and pushed far away from the blurry image in the representation space.Furthermore,instead of estimating the degradation,we extract global statistical prior information to capture the character of the distortion.Considering the coupling between the degradation and the low-resolution image,we embed the global prior into the distortion-specific SR network to make our method adaptive to the changes of distortions.We term our distortion-specific network with contrastive regularization as CRDNet.The extensive experiments on synthetic and realworld scenes demonstrate that our lightweight CRDNet surpasses state-of-the-art blind super-resolution approaches.

Hyperspectral Image Super-Resolution Meets Deep Learning:A Survey and Perspective

Hyperspectral image super-resolution,which refers to reconstructing the high-resolution hyperspectral image from the input low-resolution observation,aims to improve the spatial resolution of the hyperspectral image,which is beneficial for subsequent applications.The development of deep learning has promoted significant progress in hyperspectral image super-resolution,and the powerful expression capabilities of deep neural networks make the predicted results more reliable.Recently,several latest deep learning technologies have made the hyperspectral image super-resolution method explode.However,a comprehensive review and analysis of the latest deep learning methods from the hyperspectral image super-resolution perspective is absent.To this end,in this survey,we first introduce the concept of hyperspectral image super-resolution and classify the methods from the perspectives with or without auxiliary information.Then,we review the learning-based methods in three categories,including single hyperspectral image super-resolution,panchromatic-based hyperspectral image super-resolution,and multispectral-based hyperspectral image super-resolution.Subsequently,we summarize the commonly used hyperspectral dataset,and the evaluations for some representative methods in three categories are performed qualitatively and quantitatively.Moreover,we briefly introduce several typical applications of hyperspectral image super-resolution,including ground object classification,urban change detection,and ecosystem monitoring.Finally,we provide the conclusion and challenges in existing learning-based methods,looking forward to potential future research directions.

Improved spatiotemporal resolution of anti-scattering super-resolution label-free microscopy via synthetic wave 3D metalens imaging

Super-resolution(SR)microscopy has dramatically enhanced our understanding of biological processes.However,scattering media in thick specimens severely limits the spatial resolution,often rendering the images unclear or indistinguishable.Additionally,live-cell imaging faces challenges in achieving high temporal resolution for fast-moving subcellular structures.Here,we present the principles of a synthetic wave microscopy(SWM)to extract three-dimensional information from thick unlabeled specimens,where photobleaching and phototoxicity are avoided.SWM exploits multiple-wave interferometry to reveal the specimen’s phase information in the area of interest,which is not affected by the scattering media in the optical path.SWM achieves~0.42λ/NA resolution at an imaging speed of up to 106 pixels/s.SWM proves better temporal resolution and sensitivity than the most conventional microscopes currently available while maintaining exceptional SR and anti-scattering capabilities.Penetrating through the scattering media is challenging for conventional imaging techniques.Remarkably,SWM retains its efficacy even in conditions of low signal-to-noise ratios.It facilitates the visualization of dynamic subcellular structures in live cells,encompassing tubular endoplasmic reticulum(ER),lipid droplets,mitochondria,and lysosomes.