Cryo-EM combined with image deconvolution to determine ZIF-8 crystal structure

Metal–organic frameworks(MOFs) are crystalline porous materials with tunable properties, exhibiting great potential in gas adsorption, separation and catalysis.[1,2]It is challenging to visualize MOFs with transmission electron microscopy(TEM) due to their inherent instability under electron beam irradiation. Here, we employ cryo-electron microscopy(cryoEM) to capture images of MOF ZIF-8, revealing inverted-space structural information at a resolution of up to about 1.7A and enhancing its critical electron dose to around 20 e^(-)/A^(2). In addition, it is confirmed by electron-beam irradiation experiments that the high voltage could effectively mitigate the radiolysis, and the structure of ZIF-8 is more stable along the [100] direction under electron beam irradiation. Meanwhile, since the high-resolution electron microscope images are modulated by contrast transfer function(CTF) and it is difficult to determine the positions corresponding to the atomic columns directly from the images. We employ image deconvolution to eliminate the impact of CTF and obtain the structural images of ZIF-8. As a result, the heavy atom Zn and the organic imidazole ring within the organic framework can be distinguished from structural images.

Enhancing the vertical resolution of lunar penetrating radar data using predictive deconvolution

The Yutu-2 rover onboard the Chang’E-4 mission performed the first lunar penetrating radar detection on the farside of the Moon.The high-frequency channel presented us with many unprecedented details of the subsurface structures within a depth of approximately 50 m.However,it was still difficult to identify finer layers from the cluttered reflections and scattering waves.We applied deconvolution to improve the vertical resolution of the radar profile by extending the limited bandwidth associated with the emissive radar pulse.To overcome the challenges arising from the mixed-phase wavelets and the problematic amplification of noise,we performed predictive deconvolution to remove the minimum-phase components from the Chang’E-4 dataset,followed by a comprehensive phase rotation to rectify phase anomalies in the radar image.Subsequently,we implemented irreversible migration filtering to mitigate the noise and diminutive clutter echoes amplified by deconvolution.The processed data showed evident enhancement of the vertical resolution with a widened bandwidth in the frequency domain and better signal clarity in the time domain,providing us with more undisputed details of subsurface structures near the Chang’E-4 landing site.

Research on a deconvolution algorithm for laser-induced fluorescence diagnosis based on the maximum entropy principle

Laser-induced fluorescence(LIF)spectroscopy is employed for plasma diagnosis,necessitating the utilization of deconvolution algorithms to isolate the Doppler effect from the raw spectral signal.However,direct deconvolution becomes invalid in the presence of noise as it leads to infinite amplification of high-frequency noise components.To address this issue,we propose a deconvolution algorithm based on the maximum entropy principle.We validate the effectiveness of the proposed algorithm by utilizing simulated LIF spectra at various noise levels(signal-to-noise ratio,SNR=20–80 d B)and measured LIF spectra with Xe as the working fluid.In the typical measured spectrum(SNR=26.23 d B)experiment,compared with the Gaussian filter and the Richardson–Lucy(R-L)algorithm,the proposed algorithm demonstrates an increase in SNR of 1.39 d B and 4.66 d B,respectively,along with a reduction in the root-meansquare error(RMSE)of 35%and 64%,respectively.Additionally,there is a decrease in the spectral angle(SA)of 0.05 and 0.11,respectively.In the high-quality spectrum(SNR=43.96 d B)experiment,the results show that the running time of the proposed algorithm is reduced by about98%compared with the R-L iterative algorithm.Moreover,the maximum entropy algorithm avoids parameter optimization settings and is more suitable for automatic implementation.In conclusion,the proposed algorithm can accurately resolve Doppler spectrum details while effectively suppressing noise,thus highlighting its advantage in LIF spectral deconvolution applications.

Deblurring,artifact-free optical coherence tomography with deconvolution-random phase modulation

Deconvolution is a commonly employed technique for enhancing image quality in optical imaging methods.Unfortu-nately,its application in optical coherence tomography(OCT)is often hindered by sensitivity to noise,which leads to ad-ditive ringing artifacts.These artifacts considerably degrade the quality of deconvolved images,thereby limiting its effect-iveness in OCT imaging.In this study,we propose a framework that integrates numerical random phase masks into the deconvolution process,effectively eliminating these artifacts and enhancing image clarity.The optimized joint operation of an iterative Richardson-Lucy deconvolution and numerical synthesis of random phase masks(RPM),termed as De-conv-RPM,enables a 2.5-fold reduction in full width at half-maximum(FWHM).We demonstrate that the Deconv-RPM method significantly enhances image clarity,allowing for the discernment of previously unresolved cellular-level details in nonkeratinized epithelial cells ex vivo and moving blood cells in vivo.

A Modified Eigenvector Method for Blind Deconvolution of MIMO Systems Using the Matrix Pseudo-Inversion Lemma

Recently we have developed an eigenvector method (EVM) which can achieve the blind deconvolution (BD) for MIMO systems. One of attractive features of the proposed algorithm is that the BD can be achieved by calculating the eigenvectors of a matrix relevant to it. However, the performance accuracy of the EVM depends highly on computational results of the eigenvectors. In this paper, by modifying the EVM, we propose an algorithm which can achieve the BD without calculating the eigenvectors. Then the pseudo-inverse which is needed to carry out the BD is calculated by our proposed matrix pseudo-inversion lemma. Moreover, using a combination of the conventional EVM and the modified EVM, we will show its performances comparing with each EVM. Simulation results will be presented for showing the effectiveness of the proposed methods.

Source Quantitative Identification by Reference-Based Cubic Blind Deconvolution Algorithm

The semi-blind deconvolution algorithm improves the separation accuracy by introducing reference information.However,the separation performance depends largely on the construction of reference signals.To improve the robustness of the semi-blind deconvolution algorithm to the reference signals and the convergence speed,the reference-based cubic blind deconvolution algorithm is proposed in this paper.The proposed algorithm can be combined with the contribution evaluation to provide trustworthy guidance for suppressing satellite micro-vibration.The normalized reference-based cubic contrast function is proposed and the validity of the new contrast function is theoretically proved.By deriving the optimal step size of gradient iteration under the new contrast function,we propose an efficient adaptive step optimization method.Furthermore,the contribution evaluation method based on vector projection is presented to implement the source contribution evaluation.Numerical simulation analysis is carried out to validate the availability and superiority of this method.Further tests given by the simulated satellite experiment and satellite ground experiment also confirm the effectiveness.The signals of control moment gyroscope and flywheel were extracted,respectively,and the contribution evaluation of vibration sources to the sensitive load area was realized.This research proposes a more accurate and robust algorithm for the source separation and provides an effective tool for the quantitative identification of the mechanical vibration sources.

融合ResNet18和Deconvolution的输送带横向跑偏检测方法

为精准检测输送带横向跑偏距离和倾斜角度,提出一种融合ResNet18和Deconvolution的输送带横向跑偏检测方法。首先,设计输送带横向跑偏检测系统,给出输送带横向跑偏检测流程。接着,提出融合ResNet18与Deconvolution的输送带边缘和托辊接触特征关键点提取方法,并采用热力图突出关键点,以便于计算跑偏距离及倾斜角度。最后,搭建输送带横向跑偏检测实验平台,并对该检测方法进行相关实验。实验表明,该检测方法可减弱光照、粉尘等干扰影响,平均准确率为95.17%,优于YOLOv5和Maskrcnn LSD算法,满足带式输送机输送带横向跑偏检测精度要求。

Automated Video Generation of Moving Digits from Text Using Deep Deconvolutional Generative Adversarial Network

Generating realistic and synthetic video from text is a highly challenging task due to the multitude of issues involved,including digit deformation,noise interference between frames,blurred output,and the need for temporal coherence across frames.In this paper,we propose a novel approach for generating coherent videos of moving digits from textual input using a Deep Deconvolutional Generative Adversarial Network(DD-GAN).The DDGAN comprises a Deep Deconvolutional Neural Network(DDNN)as a Generator(G)and a modified Deep Convolutional Neural Network(DCNN)as a Discriminator(D)to ensure temporal coherence between adjacent frames.The proposed research involves several steps.First,the input text is fed into a Long Short Term Memory(LSTM)based text encoder and then smoothed using Conditioning Augmentation(CA)techniques to enhance the effectiveness of the Generator(G).Next,using a DDNN to generate video frames by incorporating enhanced text and random noise and modifying a DCNN to act as a Discriminator(D),effectively distinguishing between generated and real videos.This research evaluates the quality of the generated videos using standard metrics like Inception Score(IS),Fréchet Inception Distance(FID),Fréchet Inception Distance for video(FID2vid),and Generative Adversarial Metric(GAM),along with a human study based on realism,coherence,and relevance.By conducting experiments on Single-Digit Bouncing MNIST GIFs(SBMG),Two-Digit Bouncing MNIST GIFs(TBMG),and a custom dataset of essential mathematics videos with related text,this research demonstrates significant improvements in both metrics and human study results,confirming the effectiveness of DD-GAN.This research also took the exciting challenge of generating preschool math videos from text,handling complex structures,digits,and symbols,and achieving successful results.The proposed research demonstrates promising results for generating coherent videos from textual input.

基于聚焦变换解卷积的宽带稳健波束形成

In the field of array signal processing,uniform linear arrays(ULAs)are widely used to detect/separate a weak target and estimate its direction of arrival from interference and noise.Conventional beamforming(CBF)is robust but restricted by a wide mainlobe and high sidelobe level.Covariance-matrix-inversed beamforming techniques,such as the minimum variance distortionless response and multiple signal classification,are sensitive to signal mismatch and data snapshots and exhibit high-resolution performance because of the narrow mainlobe and low sidelobe level.Therefore,compared with the wideband CBF,this study proposes a robust focused-and-deconvolved conventional beamforming(RFD-CBF),utilizing the Richardson–Lucy(R-L)iterative algorithm to deconvolve the focused conventional beam power of a half-wavelength spaced ULA.Then,the focused-and-deconvolved beam power achieves a narrower mainlobe and lower sidelobe level while retaining the robustness of wideband CBF.Moreover,compared with the wideband CBF,RFD-CBF can obtain a higher output signal-to-noise ratio(SNR).Finally,the performance of RFD-CBF is evaluated through numerical simulation and verified by sea trial data processing.

Distributed Dynamic Load in Structural Dynamics by the Impulse-Based Force Estimation Algorithm

This paper proposes a novel approach for identifying distributed dynamic loads in the time domain.Using polynomial andmodal analysis,the load is transformed intomodal space for coefficient identification.This allows the distributed dynamic load with a two-dimensional form in terms of time and space to be simultaneously identified in the form of modal force,thereby achieving dimensionality reduction.The Impulse-based Force Estimation Algorithm is proposed to identify dynamic loads in the time domain.Firstly,the algorithm establishes a recursion scheme based on convolution integral,enabling it to identify loads with a long history and rapidly changing forms over time.Secondly,the algorithm introduces moving mean and polynomial fitting to detrend,enhancing its applicability in load estimation.The aforementioned methodology successfully accomplishes the reconstruction of distributed,instead of centralized,dynamic loads on the continuum in the time domain by utilizing acceleration response.To validate the effectiveness of the method,computational and experimental verification were conducted.

Solar image reconstruction method under atmospheric turbulence at Fuxian Lake Solar Observatory

Strong atmospheric turbulence reduces astronomical seeing,causing speckle images acquired by ground-based solar telescopes to become blurred and distorted.Severe distortion in speckle images impedes image phase deviation in the speckle masking reconstruction method,leading to the appearance of spurious imaging artifacts.Relying only on linear image degradation principles to reconstruct solar images is insufficient.To solve this problem,we propose the multiframe blind deconvolution combined with non-rigid alignment(MFBD-CNRA)method for solar image reconstruction.We consider image distortion caused by atmospheric turbulence and use non-rigid alignment to correct pixel-level distortion,thereby achieving nonlinear constraints to complement image intensity changes.After creating the corrected speckle image,we use the linear method to solve the wavefront phase,obtaining the target image.We verify the effectiveness of our method results,compared with others,using solar observation data from the 1 m new vacuum solar telescope(NVST).This new method successfully reconstructs high-resolution images of solar observations with a Fried parameter r0 of approximately 10 cm,and enhances images at high frequency.When r0 is approximately 5 cm,the new method is even more effective.It reconstructs the edges of solar graining and sunspots,and is greatly enhanced at mid and high frequency compared with other methods.Comparisons confirm the effectiveness of this method,with respect to both nonlinear and linear constraints in solar image reconstruction.This provides a suitable solution for image reconstruction in ground-based solar observations under strong atmospheric turbulence.

Potential risks of spectrum whitening deconvolution——compared with well-driven deconvolution

Deconvolution is widely used to increase the resolution of seismic data. To compare the resolution ability of conventional spectrum whitening deconvolution to thin layers with that of welldriven deconvolution, a complex sedimentary geological model was designed, and then the simulated seismic data were processed respectively by each of the two methods. The amplitude spectrum of seismic data was almost white after spectrum whitening, but the wavelet resolution was low. The amplitude spectrum after well-driven deconvolution deviated from white spectrum, but the wavelet resolution was high. Further analysis showed that if an actual reflectivity series could not well satisfy the hypothesis of white spectrum, spectrum whitening deconvolution had a potential risk of wavelet distortion, which might lead to a pitfall in high resolution seismic data interpretation. On the other hand, the wavelet after well- driven deconvolution had higher resolution both in the time and frequency domains. It is favorable for high resolution seismic interpretation and reservoir prediction.

Exploiting the point spread function for optical imaging through a scattering medium based on deconvolution method

Visual perception of humans penetrating turbid medium is hampered by scattering.Various techniques have been prompted recently to recover optical imaging through turbid materials.Among them,speckle correlation based on deconvolution is one of the most attractive methods taking advantage of high imaging quality,robustness,eas-of-use,and ease-of-integration.By exploiting the point spread function(PSF)of the scattering system,large Field-of-View,extended Depth-of-Field,noninvasiveness and spectral resoluation are now available as successful solutions for high quality and multifunctional image reconstruction.In this paper,we review the progress of imaging through a scattering medium based on deconvolution method,including the principle,the breakthrough of the limitation of the optical memory ffect,the improvement of the deconvolution algorithm and innovative applications.

A predictive deconvolution method for non-white-noise refl ectivity

Conventional predictive deconvolution assumes that the reflection coefficients of the earth conform to an uncorrelated white noise sequence. The Wiener-Hopf (WH) equation is constructed to solve the filter and eliminate the correlated components of the seismic records, attenuate multiples, and improve seismic resolution. However, in practice, the primary refl ectivity series of fi eld data rarely satisfy the white noise sequence assumption, with the result that the correlated components of the primary reflectivity series are also eliminated by traditional deconvolution. This results in signal distortion. To solve this problem, we have proposed an improved method for deconvolution. First, we estimated the wavelet correlation from seismic records using the spectrum-modeling method. Second, this wavelet autocorrelation was used to construct a new autocorrelation function which contains the correlated components caused by the existence of multiples and avoids the correlated components of the primary reflectivity series. Finally, the new autocorrelation function was brought into the WH equation, and the predictive fi lter operator was calculated for deconvolution. In this paper, we have applied this new method to simulated and field data processing, and we have compared its performance with that of traditional predictive deconvolution. Our results show that the new method can adapt to non-white refl ectivity series without changing the statistical characteristics of the primary reflection coefficient series. Compared with traditional predictive deconvolution, the new method reduces processing noise and improves fidelity, all while maintaining the ability to attenuate multiples and enhance seismic resolution.

A light field measurement system through PSF estimation by a morphology-based method

Light field imaging technology can obtain three-dimensional(3D)information of a test surface in a single exposure.Traditional light field reconstruction algorithms not only take a long time to trace back to the original image,but also require the exact parameters of the light field system,such as the position and posture of a microlens array(MLA),which will cause errors in the reconstructed image if these parameters cannot be precisely obtained.This paper proposes a reconstruction algorithm for light field imaging based on the point spread function(PSF),which does not require prior knowledge of the system.The accurate PSF derivation process of a light field system is presented,and modeling and simulation were conducted to obtain the relationship between the spatial distribution characteristics and the PSF of the light field system.A morphology-based method is proposed to analyze the overlapping area of the subimages of light field images to identify the accurate spatial location of the MLA used in the system,which is thereafter used to accurately refocus light field imaging.A light field system is built to verify the algorithm’s effectiveness.Experimental results show that the measurement accuracy is increased over 41.0%compared with the traditional method by measuring a step standard.The accuracy of parameters is also improved through a microstructure measurement with a peak-to-valley value of 25.4%and root mean square value of 23.5%improvement.This further validates that the algorithm can effectively improve the refocusing efficiency and the accuracy of the light field imaging results with the superiority of refocusing light field imaging without prior knowledge of the system.The proposed method provides a new solution for fast and accurate 3D measurement based on a light field.

Improved Channel Reciprocity for Secure Communication in Next Generation Wireless Systems

To secure the wireless connection between devices with low computational power has been a challenging problem due to heterogeneity in operating devices,device to device communication in Internet of Things(IoTs)and 5G wireless systems.Physical layer key generation(PLKG)tackles this secrecy problem by introducing private keys among two connecting devices through wireless medium.In this paper,relative calibration is used as a method to enhance channel reciprocity which in turn increases the performance of the key generation process.Channel reciprocity based key generation is emerged as better PLKG methodology to obtain secure wireless connection in IoTs and 5G systems.Circulant deconvolution is proposed as a promising technique for relative calibration to ensure channel reciprocity in comparison to existing techniques Total Least Square(TLS)and Structured Total Least Square(STLS).The proposed deconvolution technique replicates the performance of the STLS by exploiting the possibility of higher information reuse and its lesser computational complexity leads to less processing time in comparison to the STLS.The presented idea is validated by observing the relation between signalto-noise ratio(SNR)and the correlation coefficient of the corresponding channel measurements between communicating parties.

SELF-TUNING WEIGHTED MEASUREMENT FUSION WHITE NOISE DECONVOLUTION ESTIMATOR

For the multi-sensor linear discrete time-invariant stochastic systems with correlated measurement noises and unknown noise statistics,an on-line noise statistics estimator is obtained using the correlation method.Substituting it into the optimal weighted fusion steady-state white noise deconvolution estimator based on the Kalman filtering,a self-tuning weighted measurement fusion white noise deconvolution estimator is presented.By the Dynamic Error System Analysis(DESA) method,it proved that the self-tuning fusion white noise deconvolution estimator converges to the steady-state optimal fusion white noise deconvolution estimator in a realization.Therefore,it has the asymptotically global optimality.A simulation example for the tracking system with 3 sensors and the Bernoulli-Gaussian input white noise shows its effectiveness.

Nonstationary inversion-based directional deconvolution of airgun array signature

Airgun arrays are widely used in marine seismic exploration because signatures excited by airgun arrays have high energy and high-peak bubble ratio, whereas the considerable length and width of the array and ghost reflections make the airgun array signature directional. As a result, the relation of the reflection amplitude with the incident and azimuth angles is variable. This means that the directivity of the airgun array results in a nonstationary wavelet and distorts the relation of the amplitude variation with the incident and azimuth angles. To remove the directivity effect, we propose a nonstationary inversion-based directional deconvolution. At fi rst, the signature as a function of take-off angle and azimuth angle is calculated using the spatial configuration of the airgun array and the near-field signatures. Then, based on the velocity model, the time-variant take-off angles are estimated and directional fi lters are designed using the take-off angles. Finally, the directivity-dependent signatures are shaped to the signature right below the airgun array using nonstationary inversion in the directional deconvolution.

Resolution Enhancement in Ultrasonic TOFD Imaging by Combining Sparse Deconvolution and Synthetic Aperture Focusing Technique(Sparse-SAFT)

The shallow subsurface defects are difficult to be identified and quantified by ultrasonic time-of-flight diffraction(TOFD)due to the low resolution induced by pulse width and beam spreading.In this paper,Sparse-SAFT is proposed to improve the time resolution and lateral resolution in TOFD imaging by combining sparse deconvolution and synthetic aperture focusing technique(SAFT).The mathematical model in the frequency domain is established based on the l1 and l2 norm constraints,and the optimization problem is solved for enhancing time resolution.On this basis,SAFT is employed to improve lateral resolution by delay-and-sum beamforming.The simulated and experimental results indicate that the lateral wave and tip-diffracted waves can be decoupled with Sparse-SAFT.The shallow subsurface defects with a height of 3.0 mm at the depth of 3.0 mm were detected quantitatively,and the relative measurement errors of flaw heights and depths were no more than 10.3%.Compared to conventional SAFT,the time resolution and lateral resolution are enhanced by 72.5 and 56%with Sparse-SAFT,respectively.Finally,the proposed method is also suitable for improving resolution to detect the defects beyond dead zone.

H-infinity deconvolution filtering:a Krein space approach in state-space setting

This note is concerned with the H-infinity deconvolution filtering problem for linear time-varying discretetime systems described by state space models, The H-infinity deconvolution filter is derived by proposing a new approach in Krein space. With the new approach, it is clearly shown that the central deconvolution filter in an H-infinity setting is the same as the one in an H2 setting associated with one constructed stochastic state-space model. This insight allows us to calculate the complicated H-infinity deconvolution filter in an intuitive and simple way. The deconvolution filter is calculated by performing Riccati equation with the same order as that of the original system.