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.

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.

A generalized deep neural network approach for improving resolution of fluorescence microscopy images

Deep learning is capable of greatly promoting the progress of super-resolution imaging technology in terms of imaging and reconstruction speed,imaging resolution,and imagingflux.This paper proposes a deep neural network based on a generative adversarial network(GAN).The generator employs a U-Net-based network,which integrates Dense Net for the downsampling component.The proposed method has excellent properties,for example,the network model is trained with several different datasets of biological structures;the trained model can improve the imaging resolution of different microscopy imaging modalities such as confocal imaging and wide-field imaging;and the model demonstrates a generalized ability to improve the resolution of different biological structures even out of the datasets.In addition,experimental results showed that the method improved the resolution of caveolin-coated pits(CCPs)structures from 264 nm to 138 nm,a 1.91-fold increase,and nearly doubled the resolution of DNA molecules imaged while being transported through microfluidic channels.

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.

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.

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.

基于改进FeatDepth的足球运动场景无监督单目图像深度预测

为了在降低成本的同时提高图像深度信息预测的精确度,并将深度估计应用于足球运动场景,提出一种基于改进FeatDepth的足球运动场景无监督单目图像深度预测方法。首先,对原FeatDepth引入注意力机制,使模型更加关注有效的特征信息;其次,将FeatDepth中的PoseNet网络和DepthNet网络分别嵌入GAM全局注意力机制模块,为网络添加额外的上下文信息,在基本不增加计算成本的情况下提升FeatDepth模型深度预测性能;再次,为在低纹理区域和细节上获得更好的深度预测效果,由单视图重构损失与交叉视图重构损失组合而成最终的损失函数。选取KITTI数据集中Person场景较多的部分进行数据集制作并进行仿真实验,结果表明,改进后的FeatDepth模型不仅在精确度上有所提升,且在低纹理区域及细节处拥有更好的深度预测效果。最后,对比模型在足球场景下的推理效果后得出,改进后的模型在低纹理区域(足球、球门等)及细节处(肢体等)有更好的深度预测效果,实现了将基于无监督的单目深度估计模型应用于足球运动场景的目的。

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.

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.

Analysis of Object Depth Effects on Accuracy of Dimensional Shape in X and Y Directions Using Single Non-metric Image

In general, to reconstruct the accurate shape of buildings, we need at least one stereomodel （two photographs） for each building. In most cases, however, only a single non-metric photograph is available, which is usually obtained either by an amateur, such as a tourist, or from a newspaper or a post card. To evaluate the validity of 3D reconstruction from a single non-metric image, this study analyzes the effects of object depth on the accuracy of dimensional shape in X and Y directions using a single non-metric image by means of simulation technique, as this was considered to be, in most cases, a main source of data acquisition in recording and documenting buildings.

Reconstruction algorithm of super-resolution infrared image based on human vision processing mechanism

Aiming at solving the problem of low resolu- tion and visual blur in infrared imaging, a super-resolution infrared image reconstruction method using human vision processing mechanism （HVPM） was proposed. This method combined a mechanism of vision lateral inhibition with an algorithm projection onto convex sets （POCS） reconstruction, the improved vision lateral inhibition network was utilized to enhance the contrast between object and background of low-resolution image sequences, then POCS algorithm was adopted to reconstruct super- resolution image. Experimental results showed that the proposed method can significantly improve the visual effect of image, whose contrast and information entropy of reconstructed infrared images were improved by approxi- mately 5 times and 1.6 times compared with traditional POCS reconstruction algorithm, respectively.

Edge preserving super-resolution infrared image reconstruction based on L1-and L2-norms

Super-resolution （SR） is a widely used tech- nology that increases image resolution using algorithmic methods. However, preserving the local edge structure and visual quality in infrared （IR） SR images is challenging because of their disadvantages, such as lack of detail, poor contrast, and blurry edges. Traditional and advanced methods maintain the quantitative measures, but they mostly fail to preserve edge and visual quality. This paper proposes an algorithm based on high frequency layer features. This algorithm focuses on the IR image edge texture in the reconstruction process. Experimental results show that the mean gradient of the IR image reconstructed by the proposed algorithm increased by 1.5, 1.4, and 1.2 times than that of the traditional algorithm based on L1- norm, L2-norm, and traditional mixed norm, respectively. The peak signal-to-noise ratio, structural similarity index, and visual effect of the reconstructed image also improved.

Imaging performance evaluation in depth-of-interaction PET with a new method of sinogram generation:A Monte Carlo simulation study

In conventional PET systems,the parallax error degrades image resolution and causes image distortion.To remedy this,a PET ring diameter has to be much larger than the required size of field of view(FOV),and therefore the cost goes up.Measurement of depth-of-interaction(DOI)information is effective to reduce the parallax error and improve the image quality.This study is aimed at developing a practical method to incorporate DOI information in PET sinogram generation and image reconstruction processes and evaluate its efficacy through Monte Carlo simulation.An animal PET system with 30-mm long LSO crystals and 2-mm DOI measurement accuracy was simulated and list-mode PET data were collected.A sinogram generation method was proposed to bin each coincidence event to the correct LOR location according to both incident crystal indices and DOI positions of the two annihilation photons.The sinograms were reconstructed with an iterative OSMAPEM(ordered subset maximum a posteriori expectation maximization)algorithm.Two phantoms(a rod source phantom and a Derenzo phantom)were simulated,and the benefits of DOI were investigated in terms of reconstructed source diameter(FWHM)and source positioning accuracy.The results demonstrate that the proposed method works well to incorporate DOI information in data processing,which not only overcomes the image distortion problem but also significantly improves image resolution and resolution uniformity and results in satisfactory image quality.

Super-resolution reconstruction of astronomical images using time-scale adaptive normalized convolution

In this work, we describe a new multiframe Super-Resolution（SR） framework based on time-scale adaptive Normalized Convolution（NC）, and apply it to astronomical images. The method mainly uses the conceptual basis of NC where each neighborhood of a signal is expressed in terms of the corresponding subspace expanded by the chosen polynomial basis function. Instead of the conventional NC, the introduced spatially adaptive filtering kernel is utilized as the applicability function of shape-adaptive NC, which fits the local image structure information including shape and orientation. This makes it possible to obtain image patches with the same modality,which are collected for polynomial expansion to maximize the signal-to-noise ratio and suppress aliasing artifacts across lines and edges. The robust signal certainty takes the confidence value at each point into account before a local polynomial expansion to minimize the influence of outliers.Finally, the temporal scale applicability is considered to omit accurate motion estimation since it is easy to result in annoying registration errors in real astronomical applications. Excellent SR reconstruction capability of the time-scale adaptive NC is demonstrated through fundamental experiments on both synthetic images and real astronomical images when compared with other SR reconstruction methods.

融合场景深度估计和视觉传达的复杂光照图像虚拟重建

复杂光照图像虚拟中受到光照强度不均衡性影响导致重建效果不好,为了提高复杂光照图像虚拟重建效果,提出基于融合场景深度估计和视觉传达的复杂光照图像虚拟重建方法。针对不同场景深度混频光照的相互干扰采用相关匹配降噪方法实现图像降噪处理,以光照图像低亮度区域内亮度值中位数作为场景深度的参考值,采用全局特性和局部细节特征拟合的方法实现对复杂光照图像的场景深度检测和视觉跟踪拟合,采用HSV空间特征分解方法实现对不同场景中光照图片亮度通道融合处理,提取场景物体边缘、纹理等细节信息,根据场景深度检测和全局对比度融合下的视觉传达效果实现复杂光照图像虚拟重建。测试结果得知,采用该方法进行复杂光照图像虚拟重建的视觉表达能力较好,重建后的图像细节展示能力较强,能准确重建暗区域内隐藏的图像信息,两个数据集图像的峰值信噪比较高,均方根误差较低,分别为45.63 dB、53.21 dB和0.366、0.265,且重建时长短,仅为1.5 s,具有较强的重建性能。

基于彩色图像高频信息引导的深度图超分辨率重建算法研究

深度图像信息是三维场景信息的重要组成部分,然而,由于采集设备的局限性和成像环境的多样性,深度传感器获取的深度图像往往分辨率较低、高频信息较少,限制了其在各种计算机视觉任务中的进一步应用。深度图超分辨率试图提高深度图的分辨率,是一项实用而有价值的任务。同一场景下的RGB图像分辨率高,纹理信息丰富,部分深度图超分辨率算法通过引入来自同一场景下的RGB图像提供指导信息,实现了算法性能的显著提升。然而,由于RGB图像和深度图之间的模态不一致,如何充分、有效地利用RGB信息辅助深度图像进行图像超分辨率重建仍然极具挑战。为此,提出了一种基于彩色图像高频信息引导的深度图超分辨率重建算法。具体地,设计了一个高频特征提取模块来自适应地学习彩色图像中的高频信息,以指导深度图边缘的重建。另外,设计了一个特征自注意力模块来获取特征之间的全局依赖,同时提取更深层次的特征,以帮助深度图细节信息的恢复。经过跨模态融合,重组深度图像特征和彩色图像引导特征,并使用多尺度特征融合模块融合不同尺度特征之间的空间结构信息,获取包含多级感受野的重建信息。最后,通过深度重建模块,恢复相应的高分辨率深度图。公开数据集上的实验结果表明所提方法在定量和定性两方面均优于对比方法,验证了所提方法的有效性。

基于图像重建的深度估计方法

实现可靠精度的深度估计是三维目标检测方法的关键,该文提出了一种图像深度估计方法。基于深度学习方法,通过训练深度神经网络,从立体图像的一幅图像中重建另一幅图像实现深度估计,并在训练中采用最小化深度误差替代最小化视差误差,利用立体图像对的几何约束引入左右视图一致性损失实现更加精确的深度估计。针对图像真实深度数据获取困难、数据集制作成本高的问题,构建了基于图像重建的自监督训练的图像深度估计框架,不需要图像真实深度数据,节省了数据集制作成本;针对深度估计误差随深度的增加急剧增大的问题,采用最小化深度误差替代最小化视差误差,解决了深度估计网络过分强调近处的微小深度误差而忽略远处深度误差的问题。另外,该文还充分利用了立体图像对的几何约束,在训练中引入左右视图一致性损失来提高深度估计的准确性。实验验证了提出的图像深度估计方法在性能上优于现有的其他方法,对远处区域和细小目标进行深度估计时具有更好的性能。