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.

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.

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.

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.

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.

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.

双鉴别器盲超分重建方法研究

图像超分变率重建方法在公共安全检测、卫星成像、医学和照片恢复等方面有着十分重要的用途。该文对基于生成对抗网络的超分辨率重建方法进行研究,提出一种基于纯合成数据训练的真实世界盲超分算法(RealESRGAN)的UNet3+双鉴别器Real-ESRGAN方法(Double Unet3+Real-ESRGAN, DU3-Real-ESRGAN)。首先,在鉴别器中引入UNet3+结构,从全尺度捕捉细粒度的细节和粗粒度的语义。其次,采用双鉴别器结构,一个鉴别器学习图像纹理细节,另一个鉴别器关注图像边缘,实现图像信息互补。在Set5, Set14, BSD100和Urban100数据集上,与多种基于生成对抗网络的超分重建方法相比,除Set5数据集外,DU3-Real-ESRGAN方法在峰值信噪比(PSNR)、结构相似性(SSIM)和无参图像考评价指标(NIQE)都优于其他方法,产生了更直观逼真的高分辨率图像。

Sub-Rayleigh imaging via undersampling scanning based on sparsity constraints

We demonstrate that, by undersampling scanning object with a reconstruction algorithm related to compressed sensing, an image with the resolution exceeding the finest resolution defined by the numerical aperture of the system can be obtained. Experimental results show that the measurements needed to achieve sub-Rayleigh resolution enhancement can be less than 10% of the pixels of the object. This method offers a general approach applicable to point-by-point illumination super-resolution techniques.

Temporal compressive super-resolution microscopy at frame rate of 1200 frames per second and spatial resolution of 100 nm

Various super-resolution microscopy techniques have been presented to explore fine structures of biological specimens.However,the super-resolution capability is often achieved at the expense of reducing imaging speed by either point scanning or multiframe computation.The contradiction between spatial resolution and imaging speed seriously hampers the observation of high-speed dynamics of fine structures.To overcome this contradiction,here we propose and demonstrate a temporal compressive super-resolution microscopy(TCSRM)technique.This technique is to merge an enhanced temporal compressive microscopy and a deep-learning-based super-resolution image reconstruction,where the enhanced temporal compressive microscopy is utilized to improve the imaging speed,and the deep-learning-based super-resolution image reconstruction is used to realize the resolution enhancement.The high-speed super-resolution imaging ability of TCSRM with a frame rate of 1200 frames per second(fps)and spatial resolution of 100 nm is experimentally demonstrated by capturing the flowing fluorescent beads in microfluidic chip.Given the outstanding imaging performance with high-speed super-resolution,TCSRM provides a desired tool for the studies of high-speed dynamical behaviors in fine structures,especially in the biomedical field.

基于卷积盲降噪的混合式核磁共振成像

为了解决图像压缩感知重建研究领域中通过有效的图像先验信息重构与原图相似性高且保留细节消除伪影的高质量图像的问题,针对不足采样的K空间数据,在经典的CNN算法CBDNet算法的基础上,通过融合深度学习先验信息及传统图像恢复各自优势的方法,研究了基于深度神经网络去噪先验和BM3D块压缩感知算法的混合式重构算法.该算法采用交互式方法训练多尺度残差网络抑制噪声水平,借优化选择的方式将深度学习与传统块匹配多尺度结合以提取图像不同尺度的特征数据从而实现抑制伪影、快速重建高质量MRI.实结果表明深度学习结合BM3D在MR图像重构领域能够有效降低伪影保留细节信息,加强重构效果.与此同时,通过采用GPU的加速运算,算法的计算复杂度较使用单一算法并未增加很多.可见基于卷积盲降噪的混合式核磁共振成像效果更佳.

自适应光学图像事后重建技术研究进展

为进一步提高自适应光学系统的成像质量,本文针对目前广泛使用的盲解卷积,相位差法和斑点重建技术开展了深入研究;详细分析了以上三种技术的各自特点、应用场景和处理对象,并结合自适应光学系统的特点,有针对性的加以算法改进;实验采用自适应光学人眼视网膜细胞图像和自适应光学太阳黑子图像进行算法验证,结果表明经改进后的图像处理技术可以有效提高自适应光学图像的质量和分辨力,较好的满足了自适应光学系统对图像事后处理的需求。

基于精确探针模型的AFM图像重构研究

原子力显微镜技术已在纳米成像中得到了普遍应用。但实验表明,AFM图像在水平方向分辨率较低,其中探针针尖形貌是影响扫描图像分辨率的关键因素之一。为了提高AFM扫描图像的分辨率,改善成像质量,一种可行的方法是通过建立探针模型后,重构扫描图像。在已有的探针建模方法中,普遍采用盲建模算法。针对目前盲建模算法中降噪阈值难以优化问题,提出了一种降噪阈值最优估计新方法。该方法可以使盲建模算法更准确地建立扫描方向上的探针形貌轮廓,进而完成3D探针模型。通过应用AFM探针扫描多空铝和标准栅格实验,介绍了探针针尖形貌精确建模的方法。然后使用数学形态学的腐蚀运算对标准栅格的AFM成像进行了重构,验证了上述方法的有效性。实验结果证明,重构后的图像中降低了探针针尖形貌的失真影响,可以显著改善扫描探针显微镜成像的水平分辨率。

基于数学形态学方法的AFM探针建模研究

AFM扫描图像可被认为是探针针尖的形貌和扫描样品表面形貌的数学形态学卷积结果,需要用反卷积的方法排除扫描图像中探针形貌引起的失真影响。本文在已有基于数学形态学的探针盲建模算法基础上,提出了一种可快速实现特征点优化提取的方法,同时提出了一种可降低最优降噪阈值估计复杂性的基于临界阈值搜索新方法。最后给出了仿真与CNT扫描图像的重构实验结果。实验表明,本文介绍的方法提高了探针建模的计算速度和建模精度,可以对AFM成像质量进行有效的失真修正和改善。

基于最大后验概率的SAR图像自适应超分辨率盲重建

提出了一种SAR图像自适应超分辨率(SR)盲重建算法。首先给出了一种图像退化过程中模糊核函数的选择策略;然后基于最大后验概率提出了新的SAR图像配准与SR盲重建的框架;再次,针对每一幅低分辨率图像引入了重要性权值并随之给出了迭代算法的总体框架,使得算法具有自适应性。试验表明,算法在SAR图像SR重建上取得了良好的效果,并且具有较好的收敛性。

基于双正则化的图像超分辨率盲重建

图像超分辨率重建是利用数字信号处理技术由一系列低分辨率观测图像得到高分辨率图像。大多数重建算法假设成像系统的模糊特性也即点扩散函数(PSF)已知,然而实际的应用环境下PSF事先不知道或部分知道。为此,将未知PSF模型化,提出基于双正则化的图像超分辨率盲重建算法,并且正则化作用的强度随重建图像局部光滑程度的变化而自适应地改变,以便能保护图像细节同时抑制平滑区域的噪声。求解过程中采用交替最小化方法估计PSF参数和高分辨率图像,并随着迭代次数的增加逐步提高每次寻优的精度以节省计算开销。实验结果表明,该算法能够比较准确地估计出PSF参数并取得较好的图像重建效果。