Super-resolution Reconstruction Algorithm Research Articles

High-Precision Electrical Impedance Tomography for Electrical Conductivity of Metallic Materials

Metal materials are subject to deformation, internal stress distribution, and cracking during processing, all of which affect the distribution of electrical conductivity of the metal. Suppose we can detect the conductivity distribution of metal materials in real time. In that case, we can complete the inverse imaging of metal material properties, structures, cracks, etc. and realize nondestructive flaw detection. However, metal materials' small resistance, high electrical conductivity, and susceptibility of voltage signals to noise signal interference make an accurate measurement of metal conductivity challenging. Therefore, this paper addresses the problem of detecting the conductivity distribution of metals by investigating a high-precision four-electrode AC measurement method. This technical approach combines laminar imaging techniques with high-precision weak signal extraction methods. On this basis, a method and equipment for high-precision electrical impedance tomography of metallic materials’ electrical conductivity were established. The way specifies a new number of electrodes and adopts a model of spaced excitation reference measurements. Single-frequency sinusoidal AC signal is used for excitation, and Shannon wavelet analysis is used for signal extraction and noise reduction. Super-resolution reconstruction algorithms are used for resistivity distribution image reconstruction to improve image quality. Based on the results of various comparative experiments, it is clear that this new functional technique method has good imaging stability and operability and can perform tasks such as analyzing the internal conductivity distribution of metals. This research provides an effective way of new ideas for the safe detection of metal structures, the changes in crystal tissue structure, and the study of metal properties. In particular, it expands the scope of research in the development and application of resistance tomography, which has tremendous commercial potential research significance.

SIM reconstruction framework for high-speed multi-dimensional super-resolution imaging.

Structured illumination microscopy (SIM) holds great promise for live cell imaging applications due to its potential to obtain multidimensional information such as intensity, spectrum and polarization (I, λ , p) at high spatial-temporal resolution, enabling the observation of more complex dynamic interactions between subcellular structures. However, the reconstruction results of polarized samples are prone to artifacts because all current SIM reconstruction frameworks use incomplete imaging models which neglect polarization modulation. Such polarization-related artifacts are especially prevalent for SIM reconstruction using a reduced number of raw images (RSIM) and severely undermine the ability of SIM to capture multi-dimensional information. Here, we report a new SIM reconstruction framework (PRSIM) that can recover multi-dimensional information (I, λ, p) using a reduced number of raw images. PRSIM adopts a complete imaging model that is versatile for normal and polarized samples and uses a frequency-domain iterative reconstruction algorithm for artifact-free super-resolution (SR) reconstruction. It can simultaneously obtain the SR spatial structure and polarization orientation of polarized samples using 6 raw SIM images and can perform SR reconstruction using 4 SIM images for normal samples. In addition, PRSIM has less spatial computational complexity and achieves reconstruction speeds tens of times higher than that of the state-of-the-art non-iterative RSIM, making it more suitable for large field-of-view imaging. Thus, PRSIM is expected to facilitate the development of SIM into an ultra-high-speed and multi-dimensional SR imaging tool.

Superresolution Reconstruction of Remote Sensing Image Based on Generative Adversarial Network

To recreate high-resolution, more detailed remote sensing images from existing low-resolution photos, this technique is known as remote sensing image superresolution reconstruction, and it has numerous uses. As an important research hotspot of neural networks, generative adversarial network (GAN) has made outstanding progress for image superresolution reconstruction. It solves the computational complexity and low reconstructed image quality of standard superresolution reconstruction algorithms. This research offers a superresolution reconstruction strategy with a self-attention generative adversarial network to improve the quality of reconstructed superresolution remote sensing images. The self-attention strategy as well as residual module is utilized to build a generator in this model that transforms low-resolution remote sensing images into superresolution ones. It aims to determine the discrepancy between a reconstructed picture and a true picture by using a deep convolutional network as a discriminator. For the purpose of enhancing the accuracy, content loss is used. This is done to obtain accurate detail reconstruction. According to the findings of the experiments, this approach is capable of regenerating higher-quality images.

Super-resolution reconstruction of digital rock CT images based on residual attention mechanism

Computer tomography technology is widely used in geological exploration because it is a nondestructive and three-dimensional imaging method that can be integrated with computer simulation. However, the large-scale application of the computer tomography technique is limited by economic costs and time consumption. Therefore, it is challenging and intractable to indicate the pore structure characteristics of rock. To address this issue, a super-resolution reconstruction algorithm based on convolutional neural networks, residual learning, and attention mechanism was proposed to generate super-resolution images in this study. This algorithm was applied to the reconstruction of carbonate rock and sandstone. The performance of two-dimensional image reconstruction was evaluated by quantitative extraction and qualitative visualization. The results from experiments indicate that the built model performs well on different upscaling factors and is superior to the existing super-resolution approaches based on convolutional neural network. Cited as: Shan, L., Bai, X., Liu, C., Feng, Y., Liu, Y., Qi, Y. Super-resolution reconstruction of digital rock CT images based on residual attention mechanism. Advances in Geo-Energy Research, 2022, 6(2): 157-168. https://doi.org/10.46690/ager.2022.02.07

Super-resolution acquisition and reconstruction for cone-beam SPECT with low-resolution detector

Background and objectiveSingle-photon emission computed tomography (SPECT) imaging, which provides information that reflects the human body's metabolic processes, has unique application value in disease diagnosis and efficacy evaluation. The imaging resolution of SPECT can be improved by exploiting high-performance detector hardware, but this exploitation generates high research and development costs. In addition, the inherent hardware structure of SPECT requires the use of a collimator, which limits the resolution in SPECT. The objective of this study is to propose a novel super-resolution (SR) reconstruction algorithm with two acquisition methods for cone-beam SPECT with low-resolution (LR) detector. MethodsA SR algorithm with two acquisition methods is proposed for cone-beam SPECT imaging in the projection domain. At each sampling angle, multi LR projections can be obtained by regularly moving the LR detector. For the two proposed acquisition methods, we develop a new SR reconstruction algorithm. Using our SR algorithm, a SR projection with the corresponding sampling angle can be obtained from multi LR projections via multi-iterations, and then, the SR SPECT image can be reconstructed. The peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), signal-to-noise ratio (SNR) and contrast recovery coefficient (CRC) are used to evaluate the final reconstruction quality. ResultsThe simulation results obtained under clean and noisy conditions verify the effectiveness of our SR algorithm. Three different phantoms are verified separately. 16 LR projections are obtained at each sampling angle, each with 32 × 32 bins. The high-resolution (HR) projection has 128 × 128 bins. The reconstruction result of the SR algorithm obtains an evaluation value that is almost the same as that of the HR reconstruction result. Our results indicate that the resolution of the resulting SPECT image is almost four times higher. ConclusionsThe authors develop a SR reconstruction algorithm with two acquisition methods for the cone-beam SPECT system. The simulation results obtained in clean and noisy environments prove that the SR algorithm has potential value, but it needs to be further tested on real equipment.

Super-Resolution Reconstruction of Brain MR Images Using Pseudo-3D Convolutional Network

The medical image super-resolution (SR) reconstruction algorithm based on deep learning with standard 3D convolution has too many network parameters. This leads to high computational complexity and low network training efficiency. To address these problems, a lightweight densely-connected residual 3D-CNN (P3DSRNet) combining dense residual connection with pseudo-3D convolution is proposed. Firstly, the dense residual block is used to widen the channel of the convolution layer. Hence, more feature information is transmitted to the activation function, so that the low-layer image features are spread to the higher layers to improve medical image super-resolution. Then, the pseudo-3D separable convolution strategy is employed to train the network, where the standard 3D convolution kernel is separated into multiple convolution kernels. The network is trained stage by stage, so network training converges faster. It effectively solved the problem that the number of parameters increase sharply with the added network training difficulty due to the widened dimension of standard 3D convolution. The experimental results show that the medical images reconstructed by the P3DSRNet model have clearer texture details and better visual effects, compared with those by the traditional interpolation and LRTV super-resolution algorithms. The P3DSRNet model has fewer network parameters, compared with the SRCNN3D and ReCNN super-resolution algorithms. In addition, the SR image PSNRs increase by 1.88 dB, 0.30 dB, and the SSIMs increase by 0.009 6, 0.001 1, respectively, compared with SRCNN3D and ReCNN. The P3DSRNet not only reduces the number of parameters and computational complexity, but also improves the super-resolution performances of medical images.

Adaptive Sparse Domain Selection for Weather Radar Superresolution

Accurate and high-resolution weather radar data reflecting detailed structure information of radar echo plays an important role in analysis and forecast of extreme weather. Typically, this is done using interpolation schemes, which only use several neighboring data values for computational approximation to get the estimated, resulting the loss of intense echo information. Focus on this limitation, a superresolution reconstruction algorithm of weather radar data based on adaptive sparse domain selection (ASDS) is proposed in this article. First, the ASDS algorithm gets a compact dictionary by learning the precollected data of model weather radar echo patches. Second, the most relevant subdictionaries are adaptively select for each low-resolution echo patches during the spare coding. Third, two adaptive regularization terms are introduced to further improve the reconstruction effect of the edge and intense echo information of the radar echo. Experimental results show that the ASDS algorithm substantially outperforms interpolation methods for ×2 and ×4 reconstruction in terms of both visual quality and quantitative evaluation metrics.

Random noise suppression and super-resolution reconstruction algorithm of seismic profile based on GAN

In this paper, we propose a random noise suppression and super-resolution reconstruction algorithm for seismic profiles based on Generative Adversarial Networks, in anticipation of reducing the influence of random noise and low resolution on seismic profiles. Firstly, the algorithm used the residual learning strategy to construct a de-noising subnet to accurate separate the interference noise on the basis of protecting the effective signal. Furthermore, it iterated the back-projection unit to complete the reconstruction of the high-resolution seismic sections image, while responsed sampling error to enhance the super-resolution performance of the algorithm. For seismic data characteristics, designed the discriminator to be a fully convolutional neural network, used a larger convolution kernels to extract data features and continuously strengthened the supervision of the generator performance optimization during the training process. The results on the synthetic data and the actual data indicated that the algorithm could improve the quality of seismic cross-section, make ideal signal-to-noise ratio and further improve the resolution of the reconstructed cross-sectional image. Besides, the observations of geological structures such as fractures were also clearer.

Superresolution Reconstruction of Remote Sensing Image Based on Middle-Level Supervised Convolutional Neural Network

Image has become one of the important carriers of visual information because of its large amount of information, easy to spread and store, and strong sense of sense. At the same time, the quality of image is also related to the completeness and accuracy of information transmission. This research mainly discusses the superresolution reconstruction of remote sensing images based on the middle layer supervised convolutional neural network. This paper designs a convolutional neural network with middle layer supervision. There are 16 layers in total, and the seventh layer is designed as an intermediate supervision layer. At present, there are many researches on traditional superresolution reconstruction algorithms and convolutional neural networks, but there are few researches that combine the two together. Convolutional neural network can obtain the high-frequency features of the image and strengthen the detailed information; so, it is necessary to study its application in image reconstruction. This article will separately describe the current research status of image superresolution reconstruction and convolutional neural networks. The middle supervision layer defines the error function of the supervision layer, which is used to optimize the error back propagation mechanism of the convolutional neural network to improve the disappearance of the gradient of the deep convolutional neural network. The algorithm training is mainly divided into four stages: the original remote sensing image preprocessing, the remote sensing image temporal feature extraction stage, the remote sensing image spatial feature extraction stage, and the remote sensing image reconstruction output layer. The last layer of the network draws on the single-frame remote sensing image SRCNN algorithm. The output layer overlaps and adds the remote sensing images of the previous layer, averages the overlapped blocks, eliminates the block effect, and finally obtains high-resolution remote sensing images, which is also equivalent to filter operation. In order to allow users to compare the superresolution effect of remote sensing images more clearly, this paper uses the Qt5 interface library to implement the user interface of the remote sensing image superresolution software platform and uses the intermediate layer convolutional neural network and the remote sensing image superresolution reconstruction algorithm proposed in this paper. When the training epoch reaches 35 times, the network has converged. At this time, the loss function converges to 0.017, and the cumulative time is about 8 hours. This research helps to improve the visual effects of remote sensing images.

Magnetic Resonance Imaging Features on Deep Learning Algorithm for the Diagnosis of Nasopharyngeal Carcinoma.

The objective of this research was to investigate the application values of magnetic resonance imaging (MRI) features of the deep learning-based image super-resolution reconstruction algorithm optimized convolutional neural network (OPCNN) algorithm in nasopharyngeal carcinoma (NPC) lesion diagnosis. A total of 54 patients with NPC were selected as research objects. Based on the traditional CNN structure, OPCNN was proposed. Besides, MRI processed by the traditional CNN model and the U-net network model was introduced to be analyzed and compared with its algorithm. The used assessment parameters included volume transfer constant (Ktrans), rate constant (Kep), volume fraction (Ve), and apparent diffusion coefficient (ADC). The results showed that the values of Dice coefficient, peak signal-to-noise ratio (PSNR), and structural similarity (SSIM) of the OPCNN algorithm were significantly higher than those of the traditional CNN model and the U-net network model. Meanwhile, the difference was statistically significant (P < 0.05). Ktrans, Kep, and Ve in tumor lesions were significantly higher than those in the healthy side, while the ADC was significantly lower than that in the healthy side (P < 0.05). The sensitivity, specificity, and accuracy of dynamic contrast-enhancement magnetic resonance imaging (DCE-MRI) in the diagnosis of nasopharyngeal carcinoma staging were slightly higher than those in T2-weighted imaging (T2WI) and diffusion-weighted imaging (DWI). The diagnostic sensitivity of DCE-MRI was more than 85%, its diagnostic specificity was more than 75%, and its diagnostic accuracy was more than 90%. The AUC area of NPC diagnosed by combination of the three was significantly different from that diagnosed by single T2WI, DWI, and DCE-MRI (P < 0.05). The diagnostic accuracy of MRI based on the OPCNN algorithm for nasopharyngeal carcinoma (93.2%) was significantly higher than that of single MRI (76.4%). In summary, the OPCNN algorithm proposed in this study could improve the quality of MRI images, and the effect was better than the traditional deep learning model, which had the value of clinical promotion. The application value of DCE-MRI in the diagnosis of pathogenic lesions of nasopharyngeal carcinoma was better than conventional MRI. The combined application of T2WI, DWI, and DCE-MRI in the screening of nasopharyngeal carcinoma lesions could greatly improve the diagnostic accuracy of nasopharyngeal carcinoma.

Super-Resolution Reconstruction Algorithm of Images Based on Improved Enhanced Super-Resolution Generative Adversarial Network

Super-Resolution Reconstruction Algorithm of Images Based on Improved Enhanced Super-Resolution Generative Adversarial Network

Super-resolution reconstruction of pneumocystis carinii pneumonia images based on generative confrontation network

ObjectivePneumocystis carinii pneumonia, also known as pneumocystis carinii pneumonia (PCP), is an interstitial plasma cell pneumonia caused by pneumocystis spp. It is a conditional lung infectious disease. Because the early and correct diagnosis of PCP has a great influence on the prognosis of patients, the image processing of PCP's high-resolution CT (HRCT) is extremely important. Traditional image super-resolution reconstruction algorithms have difficulties in network training and artifacts in generated images. The super-resolution reconstruction algorithm of generative counter-networks can optimize these two problems well. MethodsIn this paper, the texture enhanced super-resolution generative adversarial network (TESRGAN) is based on a generative confrontation network, which mainly includes a generative network and a discriminant network. In order to improve the quality of image reconstruction, TESRGAN improved the structure of the Super-Resolution Generative Adversarial Network (SRGAN) generation network, removed all BN layers in SRGAN, and replaced the ReLU function with the LeakyReLU function as the nonlinear activation function of the network to avoid the disappearance of the gradient. Experimental resultsThe TESRGAN algorithm in this paper is compared with the image reconstruction results of Bicubic, SRGAN, Enhanced Deep Super-Resolution network (EDSR), and ESRGAN. Compared with algorithms such as SRGAN and EDSR, our algorithm has clearer texture details and more accurate brightness information without extending the running time. Our reconstruction algorithm can improve the accuracy of image low-frequency information. ConclusionThe texture details of the reconstruction result are clearer and the brightness information is more accurate, which is more in line with the requirements of visual sensory evaluation.

Single Image Super-Resolution Reconstruction Algorithm with Generative Adversarial Network Based on Multi-Size Convolution and Laplacian Filtering

<p indent="0mm">At present, the image super-resolution reconstruction algorithm using deep learning still faces problems such as insufficient texture perception and insufficient realism of the reconstructed image. In order to improve the quality of the reconstructed image, a single image super-resolution reconstruction algorithm with generative adversarial network based on multi-size convolution and Laplacian filtering is proposed. First, multi-size convolution feature extraction, separable Laplacian filtering and composite residual dense block are used to build a generation network which can extract more comprehensive image information. Secondly, multi-dimensional soft labels are utilized to construct the adversarial network, which can train the generative adversarial network easily and reconstruct image texture richly. Finally, the <italic>L</italic>₁ loss function and the VGG low-level features are taken to obtain the overall features in the pre-training stage and the VGG high-level features, Charbonnier loss, and generative loss are used to make the reconstruction result more meticulously during training. Div2k and Flickr2K are chosen for model training, and Set5 and other data sets are used for testing. The experiment results show that the network size of this algorithm is 40% less than USRNet and other related algorithms, and the perceptual index is 0.76% lower than USRNet on average. The reconstruction result has more details and is more authentic.

SAR image super-resolution reconstruction based on cross-resolution discrimination

High-resolution (HR) synthetic aperture radar (SAR) images play an important role in people’s daily life and military applications. However, due to the interference of speckle noise, the texture details of the SAR images become relatively blurred. The fine texture details can be reconstructed by increasing the resolution of the SAR images. Generative adversarial networks achieve high performance in image super-resolution (SR) reconstruction, but the existing generative adversarial networks only pay attention to the discrimination of HR images without that of the low-resolution (LR) images. If the reconstructed HR image is sufficiently realistic, the LR image obtained from downsampled super-resolved images should also be the same as the original LR image. To take advantage of the LR image, an SAR image SR reconstruction algorithm based on cross-resolution discrimination (CRD) using teacher–student network is proposed. First, the teacher discriminator network (TD-Net) discriminates the HR images, which enriches the reconstructed HR images with more high-frequency texture details. Second, the student discriminator network (SD-Net) discriminates the LR images, which enables the reconstructed HR images to be accurately downsampled to the original LR image. Finally, the TD-Net guides the training of the SD-Net by transmitting distillation knowledge to the SD-Net, which further improves the discriminative performance of the SD-Net. Experiments on the SAR image dataset demonstrate that the performance of the proposed CRD algorithm is better than other algorithms when both the objective evaluations and subjective effects are considered.

Super-resolution reconstruction of T2-weighted thick-slice neonatal brain MRI scans.

Background and PurposeSuper‐resolutionreconstruction (SRR) can be used to reconstruct 3‐dimensional (3D) high‐resolution (HR) volume from several 2‐dimensional (2D) low‐resolution (LR) stacks of MRI slices. The purpose is to compare lengthy 2D T2‐weighted HR image acquisition of neonatal subjects with 3D SRR from several LR stacks in terms of image quality for clinical and morphometric assessments.MethodsLR brain images were acquired from neonatal subjects to reconstruct isotropic 3D HR volumes by using SRR algorithm. Quality assessments were done by an experienced pediatric radiologist using scoring criteria adapted to newborn anatomical landmarks. The Wilcoxon signed‐rank test was used to compare scoring results between HR and SRR images. For quantitative assessments, morphology‐based segmentation was performed on both HR and SRR images and Dice coefficients between the results were computed. Additionally, simple linear regression was performed to compare the tissue volumes.ResultsNo statistical difference was found between HR and SRR structural scores using Wilcoxon signed‐rank test (p = .63, Z = .48). Regarding segmentation results, R 2 values for the volumes of gray matter, white matter, cerebrospinal fluid, basal ganglia, cerebellum, and total brain volume including brain stem ranged between .95 and .99. Dice coefficients between the segmented regions from HR and SRR ranged between .83 ± .04 and .96 ± .01.ConclusionQualitative and quantitative assessments showed that 3D SRR of several LR images produces images that are of comparable quality to standard 2D HR image acquisition for healthy neonatal imaging without loss of anatomical details with similar edge definition allowing the detection of fine anatomical structures and permitting comparable morphometric measurement.

Super-resolution reconstruction algorithm for aerial image data management based on deep learning

Super-resolution reconstruction algorithm for aerial image data management based on deep learning

Super-resolution PIV using multi-frame displacement information

High-resolution (HR) fluid-flow velocity information is important to reliably analyze fluid measurements in particle image velocimetry (PIV), such as the boundary layer and turbulent flow. Efforts in PIV to enhance the resolution of flow fields are mainly based on single-frame information, which follows the velocity field estimation and may influence the final reconstruction accuracy. In this study, we propose a novel super-resolution (SR) reconstruction technology from another perspective, which consists of two parts: a multi-frame imaging system and a Bayesian-based multi-frame SR reconstruction algorithm. First, a splitbased imaging system is developed to obtain particle image pairs with fixed displacements. Subsequently, we present a Bayesian-based multi-frame SR (BMFSR) reconstruction algorithm to obtain an SR particle image. Multi-frame particle images collected by the developed system are used as the input low-resolution images for the following novel SR reconstruction algorithm. Synthetic and experimental particle images have been tested to verify the performance of the proposed technology, and the results are compared with the traditional and advanced reconstruction methods in PIV. The results and comparisons show that the proposed technology successfully achieves good performance in obtaining finer particle images and a more accurate velocity field.

Single Image Super-resolution Reconstruction Algorithm Based on Deep Learning

Recover the high-resolution image from a single low-resolution image is known as single image super-resolution reconstruction (SISR). SSIR is currently a research hotspot in computer vision. In this paper, we study the feature learning module of the magnification-arbitrary network for Super-Resolution(Meta-SR), and propose an improved method of image feature learning. Its idea is multi-scale residual dense block (MRDB). We integrate it into the existing structure of the Meta-SR network. As a result, the same network has increased the receptive field, and it can obtain more detailed features of the image. This structure can be extended to other existing SISR network models. In the published and standard test set Set14, compared with the interpolation method, the average PSNR value of the algorithm in this paper increases by 3.79dB, 3.01dB and 2.85dB at amplification factors 2, 3 and 4, respectively. Through the analysis of SISR results, it can be obtained that the improved method proposed in this paper has a significant improvement in all indicators, and can more fully extract the image detail features, thereby improving the quality of the reconstructed image.

Research on insulator defect detection algorithm of transmission line based on CenterNet.

The reliability of the insulator has directly affected the stable operation of electric power system. The detection of defective insulators has always been an important issue in smart grid systems. However, the traditional transmission line detection method has low accuracy and poor real-time performance. We present an insulator defect detection method based on CenterNet. In order to improve detection efficiency, we simplified the backbone network. In addition, an attention mechanism is utilized to suppress useless information and improve the accuracy of network detection. In image preprocessing, the blurring of some detected images results in the samples being discarded, so we use super-resolution reconstruction algorithm to reconstruct the blurred images to enhance the dataset. The results show that the AP of the proposed method reaches 96.16% and the reasoning speed reaches 30FPS under the test condition of NVIDIA GTX 1080 test conditions. Compared with Faster R-CNN, YOLOV3, RetinaNet and FSAF, the detection accuracy of proposed method is greatly improved, which fully proves the effectiveness of the proposed method.

Research on Blind Super-Resolution Technology for Infrared Images of Power Equipment Based on Compressed Sensing Theory.

Infrared images of power equipment play an important role in power equipment status monitoring and fault identification. Aiming to resolve the problems of low resolution and insufficient clarity in the application of infrared images, we propose a blind super-resolution algorithm based on the theory of compressed sensing. It includes an improved blur kernel estimation method combined with compressed sensing theory and an improved infrared image super-resolution reconstruction algorithm based on block compressed sensing theory. In the blur kernel estimation method, we propose a blur kernel estimation algorithm under the compressed sensing framework to realize the estimation of the blur kernel from low-resolution images. In the estimation process, we define a new norm to constrain the gradient image in the iterative process by analyzing the significant edge intensity changes before and after the image is blurred. With the norm, the salient edges can be selected and enhanced, the intermediate latent image generated by the iteration can move closer to the clear image, and the accuracy of the blur kernel estimation can be improved. For the super-resolution reconstruction algorithm, we introduce a blur matrix and a regular total variation term into the traditional compressed sensing model and design a two-step total variation sparse iteration (TwTVSI) algorithm. Therefore, while ensuring the computational efficiency, the boundary effect caused by the block processing inside the image is removed. In addition, the design of the TwTVSI algorithm can effectively process the super-resolution model of compressed sensing with a sparse dictionary, thereby breaking through the reconstruction performance limitation of the traditional regularized super-resolution method of compressed sensing due to the lack of sparseness in the signal transform domain. The final experimental results also verify the effectiveness of our blind super-resolution algorithm.