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.

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.

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.

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 lateral super-resolution imaging method using structured illumination without phase shift

Structured illumination microscopy has been a useful method for achieving lateral super-resolution,but it typically requires at least three precise phase shifts per orientation.In this paper,we propose a super-resolution method that utilizes structured illumination without phase shift.The reconstruction process requires only a conventionally illuminated image and an image with structured illumination.This method achieves the same effect as the traditional phase shift method,and more than doubles the resolution by synthesizing a few reconstructions at different illumination frequencies.We verify the resolution improvement process using a combination of theoretical derivations and diagrams,and demonstrate its effectiveness with numerical simulations.

Method of lateral image reconstruction in structured illumination microscopy with super resolution

The image reconstruction process in super-resolution structured illumination microscopy(SIM)is investigated.The structured pattern is generated by the interference of two Gaussian beams to encode undetectable spectra into detectable region of microscope.After parameters estimation of the structured pattern,the encoded spectra are computationally decoded and recombined in Fourier domain to equivalently increase the cut-off frequency of microscope,resulting in the extension of detectable spectra and a reconstructed image with about two-fold enhanced resolution.Three di®erent methods to estimate the initial phase of structured pattern are compared,verifying the auto-correlation algorithm a®ords the fast,most precise and robust measurement.The artifacts sources and detailed reconstruction°owchart for both linear and nonlinear SIM are also presented.

Combination of super-resolution reconstruction and SGA-Net for marsh vegetation mapping using multi-resolution multispectral and hyperspectral images

Vegetation is crucial for wetland ecosystems.Human activities and climate changes are increasingly threatening wetland ecosystems.Combining satellite images and deep learning for classifying marsh vegetation communities has faced great challenges because of its coarse spatial resolution and limited spectral bands.This study aimed to propose a method to classify marsh vegetation using multi-resolution multispectral and hyperspectral images,combining super-resolution techniques and a novel self-constructing graph attention neural network(SGA-Net)algorithm.The SGA-Net algorithm includes a decoding layer(SCE-Net)to preciselyfine marsh vegetation classification in Honghe National Nature Reserve,Northeast China.The results indicated that the hyperspectral reconstruction images based on the super-resolution convolutional neural network(SRCNN)obtained higher accuracy with a peak signal-to-noise ratio(PSNR)of 28.87 and structural similarity(SSIM)of 0.76 in spatial quality and root mean squared error(RMSE)of 0.11 and R^(2) of 0.63 in spectral quality.The improvement of classification accuracy(MIoU)by enhanced super-resolution generative adversarial network(ESRGAN)(6.19%)was greater than that of SRCNN(4.33%)and super-resolution generative adversarial network(SRGAN)(3.64%).In most classification schemes,the SGA-Net outperformed DeepLabV3+and SegFormer algorithms for marsh vegetation and achieved the highest F1-score(78.47%).This study demonstrated that collaborative use of super-resolution reconstruction and deep learning is an effective approach for marsh vegetation mapping.

Super-resolution microscopy and its applications in neuroscience

Optical microscopy promises researchers to soe most tiny substances directly.However,the resolution of conventional microscopy is resticted by the diffraction limit.This makes it a challenge to observe subcellular processes happened in nanoscale.The development of super-resolution microscopy provides a solution to this challenge.Here,we briefly review several commonly used super-resolution techniques,explicating their basic principles and applications in biological science,especially in neuroscience.In addition,characteristics and limitations of each techrique are compared to provide a guidance for biologists to choose the most suitable tool.

Super-Resolution with Multiselective Contourlets

We introduce a new approach to image super-resolution. The idea is to use a simple wavelet-based linear interpolation scheme as our initial estimate of high-resolution image;and to intensify geometric structure in initial estimation with an iterative projection process based on hard-thresholding scheme in a new angular multiselectivity domain. This new domain is defined by combining of laplacian pyramid and angular multiselectivity decomposition, the result is multiselective contourlets which can capture and restore adaptively and slightly better geometric structure of image. The experimental results demonstrate the effectiveness of the proposed approach.

Research on the New Algorithm of Image Super Resolution Reconstruction

This paper put forward the super-resolution image algorithm based on Gauss process regression sparse solution. We establish local Gauss process regression model, to solve the feasibility problem of regression super-resolution problem in solving Gauss process; further use sparse algorithm, not only it can optimize the super parameter of Gauss kernel function, but also to optimize the initial entry training, so as to obtain more accurate regression Gauss process. Experimental results show that： the paper proposed algorithm can does not reduce the image reconstruction results, and it can reduce the computational complexity.

基于ACFM单传感器的裂纹角度识别与重构

交变电磁场测量(ACFM)技术广泛应用于海洋装备和制造业等领域中金属结构物的缺陷检测。针对单传感器检测过程中存在的角度偏转和裂纹定位等问题展开了研究。首先,通过COMSOL Multi-physics仿真结果可知B_(x)和B_(y)在多角度偏转的过程中存在互补的规律;然后,通过建立比例因子,实现高精度的数值拟合。此外,通过多梯度偏转仿真实现了偏转裂纹的重构。最终通过搭建实验平台和信号特征提取等工作验证了拟合规律的合理性;通过优化拟合公式,提高了检测精度;通过所测角度,进而解决了非平行检测过程中裂纹重构图像的偏转问题。

联合傅里叶卷积与通道注意力的光场角度重建

光场相机能够同时捕获光线的强度和方向信息,但由于成像传感器尺寸的限制,无法同时获得高空间和角度分辨率的光场图像。提出了一种联合傅里叶卷积和通道注意力的光场角度重建方法,通过使用稀疏光场图像4个边角位置的参考视图,可以间接地重建出密集光场图像。考虑到光场数据的内在4D结构,采用通道级密集快速傅里叶残差卷积块,在空域和频域对光场图像的空间和角度相关性进行建模,然后采用基于全局响应归一化的通道注意块,以实现通道间的自适应融合。此外,还提出了一种改进的视点加权间接合成方法,通过为每个参考视图分配一个置信图,为参考视图之间建立联系以合成更真实的新视图。实验结果表明,相比于现有先进的光场角度重建算法IRVAE,所提方法的重建光场图像质量在自然光场数据集30Scenes,Occlusion和Reflective上的平均PSNR分别提高了0.08,0.13和0.13 dB。所提方法在保证光场角度一致性的前提下取得了清晰的重建结果。

基于旋转变分模态分解的IMU角速度去噪算法

惯性测量单元(IMU)的应用中,角速度的噪声误差积累对姿态解算性能有较大的影响。针对角速度中存在的噪声,提出一种融合了变分模态分解(VMD)和角速度旋转三维分解的去噪算法。首先通过坐标系旋转获得角速度在不同虚拟轴的输出,再利用VMD提取合适分量重构虚拟轴信号。VMD的非线性重构使得各个虚拟轴的残留误差相对独立,最终多个虚拟轴的反向旋转回到原始坐标系后通过独立信号的均值合并能有效消除IMU中角速度的噪声。基于EuRoC数据集的实验结果表明:该算法降噪效果显著,均方根误差降低70%~85%,且能有效平衡三轴误差。

Deep neural network based on multi-level wavelet and attention for structured illumination microscopy

Structured illumination microscopy(SIM)is a popular and powerful super-resolution(SR)technique in biomedical research.However,the conventional reconstruction algorithm for SIM heavily relies on the accurate prior knowledge of illumination patterns and signal-to-noise ratio(SNR)of raw images.To obtain high-quality SR images,several raw images need to be captured under high fluorescence level,which further restricts SIM’s temporal resolution and its applications.Deep learning(DL)is a data-driven technology that has been used to expand the limits of optical microscopy.In this study,we propose a deep neural network based on multi-level wavelet and attention mechanism(MWAM)for SIM.Our results show that the MWAM network can extract high-frequency information contained in SIM raw images and accurately integrate it into the output image,resulting in superior SR images compared to those generated using wide-field images as input data.We also demonstrate that the number of SIM raw images can be reduced to three,with one image in each illumination orientation,to achieve the optimal tradeoff between temporal and spatial resolution.Furthermore,our MWAM network exhibits superior reconstruction ability on low-SNR images compared to conventional SIM algorithms.We have also analyzed the adaptability of this network on other biological samples and successfully applied the pretrained model to other SIM systems.