Single frame super-resolution reconstruction based on sparse representation

In order to effectively improve the quality of recovered images, a single frame super-resolution reconstruction method based on sparse representation is proposed. The combination method of local orientation estimation-based image patch clustering and principal component analysis is used to obtain a series of geometric dictionaries of different orientations in the dictionary learning process. Subsequently, the dictionary of the nearest orientation is adaptively assigned to each of the input patches that need to be represented in the sparse coding process. Moreover, the consistency of gradients is further incorporated into the basic framework to make more substantial progress in preserving more fine edges and producing sharper results. Two groups of experiments on different types of natural images indicate that the proposed method outperforms some state-of- the-art counterparts in terms of both numerical indicators and visual quality.

Super-resolution reconstruction of synthetic-aperture radar image using adaptive-threshold singular value decomposition technique

A super-resolution reconstruction approach of （SVD） technique was presented, and its performance was radar image using an adaptive-threshold singular value decomposition analyzed, compared and assessed detailedly. First, radar imaging model and super-resolution reconstruction mechanism were outlined. Then, the adaptive-threshold SVD super-resolution algorithm, and its two key aspects, namely the determination method of point spread function （PSF） matrix T and the selection scheme of singular value threshold, were presented. Finally, the super-resolution algorithm was demonstrated successfully using the measured synthetic-aperture radar （SAR） images, and a Monte Carlo assessment was carried out to evaluate the performance of the algorithm by using the input/output signal-to-noise ratio （SNR）. Five versions of SVD algorithms, namely 1 ） using all singular values, 2） using the top 80% singular values, 3） using the top 50% singular values, 4） using the top 20% singular values and 5） using singular values s such that S2≥/max（s2）/rinsNR were tested. The experimental results indicate that when the singular value threshold is set as Smax/（rinSNR）1/2, the super-resolution algorithm provides a good compromise between too much noise and too much bias and has good reconstruction results.

Time-domain wavefield reconstruction inversion

Wave field reconstruction inversion （WRI） is an improved full waveform inversion theory that has been proposed in recent years. WRI method expands the searching space by introducing the wave equation into the objective function and reconstructing the wavefield to update model parameters, thereby improving the computing efficiency and mitigating the influence of the local minimum. However, frequency-domain WRI is difficult to apply to real seismic data because of the high computational memory demand and requirement of time-frequency transformation with additional computational costs. In this paper, wavefield reconstruction inversion theory is extended into the time domain, the augmented wave equation of WRI is derived in the time domain, and the model gradient is modified according to the numerical test with anomalies. The examples of synthetic data illustrate the accuracy of time-domain WRI and the low dependency of WRI on low-frequency information.

Cone-beam local reconstruction based on a Radon inversion transformation

The local reconstruction from truncated projection data is one area of interest in image reconstruction for com- puted tomography （CT）, which creates the possibility for dose reduction. In this paper, a filtered-backprojection （FBP） algorithm based on the Radon inversion transform is presented to deal with the three-dimensional （3D） local recon- struction in the circular geometry. The algorithm achieves the data filtering in two steps. The first step is the derivative of projections, which acts locally on the data and can thus be carried out accurately even in the presence of data trun- cation. The second step is the nonlocal Hilbert filtering. The numerical simulations and the real data reconstructions have been conducted to validate the new reconstruction algorithm. Compared with the approximate truncation resistant algorithm for computed tomography （ATRACT）, not only it has a comparable ability to restrain truncation artifacts, but also its reconstruction efficiency is improved. It is about twice as fast as that of the ATRACT. Therefore, this work provides a simple and efficient approach for the approximate reconstruction from truncated projections in the circular cone-beam CT.

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.

Super-resolution image reconstruction based on three-step-training neural networks

A new method of super-resolution image reconstruction is proposed, which uses a three-step-training error backpropagation neural network （BPNN） to realize the super-resolution reconstruction （SRR） of satellite image. The method is based on BPNN. First, three groups learning samples with different resolutions are obtained according to image observation model, and then vector mappings are respectively used to those three group learning samples to speed up the convergence of BPNN, at last, three times consecutive training are carried on the BPNN. Training samples used in each step are of higher resolution than those used in the previous steps, so the increasing weights store a great amount of information for SRR, and network performance and generalization ability are improved greatly. Simulation and generalization tests are carried on the well-trained three-step-training NN respectively, and the reconstruction results with higher resolution images verify the effectiveness and validity of this method.

Multi-channel fast super-resolution image reconstruction based on matrix observation model

A multi-channel fast super-resolution image reconstruction algorithm based on matrix observation model is proposed in the paper,which consists of three steps to avoid the computational complexity: a single image SR reconstruction step,a registration step and a wavelet-based image fusion. This algorithm decomposes two large matrixes to the tensor product of two little matrixes and uses the natural isomorphism between matrix space and vector space to transform cost function based on matrix-vector products model to matrix form. Furthermore,we prove that the regularization part can be transformed to the matrix formed. The conjugate-gradient method is used to solve this new model. Finally,the wavelet fusion is used to integrate all the registered highresolution images obtained from the single image SR reconstruction step. The proposed algorithm reduces the storage requirement and the calculating complexity,and can be applied to large-dimension low-resolution images.

A NOVEL METHOD TO REALIZE COMPRESSED VIDEO SUPER-RESOLUTION RECONSTRUCTION

This letter proposes a novel method of compressed video super-resolution reconstruction based on MAP-POCS (Maximum Posterior Probability-Projection Onto Convex Set). At first assuming the high-resolution model subject to Poisson-Markov distribution, then constructing the projecting convex based on MAP. According to the characteristics of compressed video, two different convexes are constructed based on integrating the inter-frame and intra-frame information in the wavelet-domain. The results of the experiment demonstrate that the new method not only outperforms the traditional algorithms on the aspects of PSNR (Peak Signal-to-Noise Ratio), MSE (Mean Square Error) and reconstruction vision effect, but also has the advantages of rapid convergence and easy extension.

Wavefield reconstruction inversion with weaktotal-variation constraint based on first arrival traveltime tomography

The objective function of full waveform inversion is a strong nonlinear function,the inversion process is not unique,and it is easy to fall into local minimum.Firstly,in the process of wavefield reconstruction,the wave equation is introduced into the construction of objective function as a penalty term to broaden the search space of solution and reduce the risk of falling into local minimum.In addition,there is no need to calculate the adjoint wavefield in the inversion process,which can significantly improve the calculation efficiency;Secondly,considering that the total variation constraint can effectively reconstruct the discontinuous interface in the velocity model,this paper introduces the weak total variation constraint to avoid the excessive smooth estimation of the model under the strong total variation constraint.The disadvantage of this strategy is that it is highly dependent on the initial model.In view of this,this paper takes the long wavelength initial model obtained by first arrival traveltime tomography as a prior model constraint,and proposes a weak total variation constrained wavefield reconstruction inversion method based on first arrival traveltime tomography.Numerical experimental results show that the new method reduces the dependence on the initial model,the interface description is more accurate,the error is reduced,and the iterative convergence efficiency is significantly improved.

Super-resolution reconstruction based on CNN:A case study of Jilin-1 multispectral data

MS or MS+PAN is usually applied separately in convolutional neural network(CNN)resolution reconstruction to obtain high-resolution MS images,but the difference between the two datasets is rarely studied.This paper introduced a dual-channel network and took MS and MS+PAN of Jilin-1 spectrum satellites as two datasets to evaluate the performance of CNN resolution reconstruction,and analyzed the difference with bicubic and GS methods.The result of CNN reconstruction shows that MS+PAN dataset performed better than MS,with about 6%improvement in spatial and spectral components,and the overall quality of MS+PAN dataset was slightly higher than that of MS dataset,with QNR from 0.9559 to 0.9584.The bicubic performed best in spectral components with the quality value of 0.017,and GS performed best in spatial components with the quality values of 0.0443.CNN showed similar performance in spectral and spatial components with the two traditional methods and achieved the best overall quality with QNR value of 0.9584.

Transformer and GAN-Based Super-Resolution Reconstruction Network for Medical Images

Super-resolution reconstruction in medical imaging has become more demanding due to the necessity of obtaining high-quality images with minimal radiation dose,such as in low-field magnetic resonance imaging(MRI).However,image super-resolution reconstruction remains a difficult task because of the complexity and high textual requirements for diagnosis purpose.In this paper,we offer a deep learning based strategy for reconstructing medical images from low resolutions utilizing Transformer and generative adversarial networks(T-GANs).The integrated system can extract more precise texture information and focus more on important locations through global image matching after successfully inserting Transformer into the generative adversarial network for picture reconstruction.Furthermore,we weighted the combination of content loss,adversarial loss,and adversarial feature loss as the final multi-task loss function during the training of our proposed model T-GAN.In comparison to established measures like peak signal-to-noise ratio(PSNR)and structural similarity index measure(SSIM),our suggested T-GAN achieves optimal performance and recovers more texture features in super-resolution reconstruction of MRI scanned images of the knees and belly.

Deep-learning-based methods for super-resolution fluorescence microscopy

The algorithm used for reconstruction or resolution enhancement is one of the factors affectingthe quality of super-resolution images obtained by fluorescence microscopy.Deep-learning-basedalgorithms have achieved stateof-the-art performance in super-resolution fluorescence micros-copy and are becoming increasingly attractive.We firstly introduce commonly-used deep learningmodels,and then review the latest applications in terms of the net work architectures,the trainingdata and the loss functions.Additionally,we discuss the challenges and limits when using deeplearning to analyze the fluorescence microscopic data,and suggest ways to improve the reliability and robustness of deep learning applications.

Preliminary analysis of reactivity reconstruction capability based on inverse kinetics method under different initial reactivity states

Online reactivity monitoring plays an important role in operation and safety analyses of fission reactor systems. The inverse kinetics method, which is based on a point kinetics model, is the most widely used method for reactivity reconstruction of critical water reactors. However, this method is seldom applied to the reactivity reconstruction of subcritical reactors. In this study, an inverse kinetics method was employed for the reactivity reconstruction of a lead-based reactor under different initial reactivity states(ρ_0= 0,-2786,-5486,-8367, and-12,371 pcm). The results showed that the deviation in the reactivity of the lead-based subcritical reactor was greater when ρ_0 became smaller. The reactivity reconstructed using the inverse kinetics method was globally underestimated. At a given reactivity perturbation, the relative and absolute errors increased with the decrease in the initial reactivity. At a given initial reactivity, with the increase in the reactivity perturbation, the absolute error increased, whereas the relative error remained the same.This deviation is due to the variation in the external neutron source, spatial-spectral effects, and sub-diffusive effects, which require further study.

Reconstruction of sampling in shift-invariant space using generalized inverse

A method that attempts to recover signal using generalized inverse theory is presented to obtain a good approximation of the signal in reconstruction space from its generalized samples. The proposed approaches differ with the assumptions on reconstruction space. If the reconstruction space satisfies one-to-one relationship between the samples and the reconstruction model, then we propose a method, which achieves consistent signal reconstruction. At the same time, when the number of samples is more than the number of reconstruction functions, the minimal-norm reconstruction signal can be obtained. Finally, it is demonstrated that the minimal-norm reconstruction can outperform consistent signal reconstruction in both theory and simulations for the problem.

An Experimental Study for Inverse Load Reconstruction

This paper performs an experimental study for inverse load reconstruction. By measuring and analyzing the load characteristics of different home and office electric devices, the author shows that a reconstruction of the individual power consumption of different loads from the total measurement of a single power meter is possible.

Reconstruction of impact force of mechanical press in time domain

To overcome the difficulty in directly measuring the impact force of a mechanical press, the inverse theory is employed to reconstruct the impact force from the corresponding response data in time domain. The nature of ill-posedness of impact force reconstruction is explored through singular value decomposition （SVD） and the Tikhonov regularization is utilized to deal with the ill-posedness, in which the optimal parameter is chosen in light of the L-curve criterion and the generalized cross- validation （GCV）. The experimentally measured strain responses of upper and lower dies of the press are chosen as source data for impact force reconstruction, and the corresponding numerical results are compared with the experimental measurements, which verifies the effectiveness of the reconstruction method.

Research on the Application of Super Resolution Reconstruction Algorithm for Underwater Image

Underwater imaging is widely used in ocean,river and lake exploration,but it is affected by properties of water and the optics.In order to solve the lower-resolution underwater image formed by the influence of water and light,the image super-resolution reconstruction technique is applied to the underwater image processing.This paper addresses the problem of generating super-resolution underwater images by convolutional neural network framework technology.We research the degradation model of underwater images,and analyze the lower-resolution factors of underwater images in different situations,and compare different traditional super-resolution image reconstruction algorithms.We further show that the algorithm of super-resolution using deep convolution networks(SRCNN)which applied to super-resolution underwater images achieves good results.

A Novel Method for the Reconstruction of Road Profiles from Measured Vehicle Responses Based on the Kalman Filter Method

The estimation of the disturbance input acting on a vehicle from its given responses is an inverse problem.To overcome some of the issues related to ill-posed inverse problems,this work proposes a method of reconstructing the road roughness based on the Kalman filter method.A half-car model that considers both the vehicle and equipment is established,and the joint input-state estimation method is used to identify the road profile.The capabilities of this methodology in the presence of noise are numerically demonstrated.Moreover,to reduce the influence of the driving speed on the estimation results,a method of choosing the calculation frequency is proposed.A road vibration test is conducted to benchmark the proposed method.

RECONSTRUCTION OF ONE DIMENSIONAL MULTI-LAYERED MEDIA BY USING A TIME DOMAIN SIGNAL FLOW GRAPH TECHNIQUE

A novel inverse scattering method to reconstruct the permittivity profile of one-dimensional multi-layered media is proposed in this paper.Based on the equivalent network ofthe medium,a concept of time domain signal flow graph and its basic principles are introduced,from which the reflection coefficient of the medium in time domain can be shown to be a series ofDirac δ-functions(pulse responses).In terms of the pulse responses,we will reconstruct both thepermittivity and the thickness of each layer will accurately be reconstructed.Numerical examplesverify the applicability of this

The Improved Simulated Annealing Algorithm and Application in the Surface Wave Dispersion Inversion

The velocity distribution of layers from surface wave dispersion curve is a severely nonlinear program. Base on the Metropolis rule,we improved the simulated annealing algorithm to simultaneously inverse the velocities and thicknesses using the dispersion data and identified the Moho and the bottom of lithosphere. The application to the numerical examples with 5% noise shows the velocity RMS is 1. 56% between the non-linear results and the original models when the condition of selecting method for temperature parameters and initial temperature are satisfied. Using the pure dispersions of Rayleigh wave,the nonlinear inversion has been carried out for S-wave velocities and thicknesses of the vertical profile crossing the Indian Plate,the Qinghai-Tibetan Plateau,and the Tarim Basin. It indicated that the crustal thickness is about 70 km in the Qiangtang block,while in the hinterland of the Qinghai-Tibetan Plateau the lithosphere is relatively thin(~ 130 km)from the velocity values and their offsets.