- 标题
- 摘要
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- 实验方案
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A robust and versatile platform for image scanning microscopy enabling super-resolution FLIM
摘要： Image scanning microscopy (ISM) can improve the effective spatial resolution of confocal microscopy to its theoretical limit. However, current implementations are not robust or versatile, and are incompatible with fluorescence lifetime imaging (FLIM). We describe an implementation of ISM based on a single-photon detector array that enables super-resolution FLIM and improves multicolor, live-cell and in-depth imaging, thereby paving the way for a massive transition from confocal microscopy to ISM.
关键词： super-resolution,SPAD array,fluorescence lifetime imaging,confocal microscopy,image scanning microscopy
更新于2025-09-23 15:23:52
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[Methods in Molecular Biology] T-Cell Motility Volume 1930 (Methods and Protocols) || Three-Dimensional Structured Illumination Microscopy (3D-SIM) to Dissect Signaling Cross-Talks in Motile T-Cells
摘要： Visualization of signal transduction events in T-cells has always been a challenge due to their miniscule size. Recent advancement in super-resolution microscopy techniques presents many new opportunities to navigate the spatial and temporal signaling cross-talks in motile T-cells. Here, we provide technical details, optimal conditions, and critical practical considerations that need to be taken into account during cell handling, sample preparation, and image acquisition of motile T-cells for performing three-dimensional structured illumination microscopy (3D-SIM).
关键词： 3D-SIM,Immunostaining,Super-resolution microscopy
更新于2025-09-23 15:22:29
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Hyperspectral Image Super-Resolution via Local Low-Rank and Sparse Representations
摘要： Remotely sensed hyperspectral images (HSIs) usually have high spectral resolution but low spatial resolution. A way to increase the spatial resolution of HSIs is to solve a fusion inverse problem, which fuses a low spatial resolution HSI (LR-HSI) with a high spatial resolution multispectral image (HR-MSI) of the same scene. In this paper, we propose a novel HSI super-resolution approach (called LRSR), which formulates the fusion problem as the estimation of a spectral dictionary from the LR-HSI and the respective regression coefficients from both images. The regression coefficients are estimated by formulating a variational regularization problem which promotes local (in the spatial sense) low-rank and sparse regression coefficients. The local regions, where the spectral vectors are low-rank, are estimated by segmenting the HR-MSI. The formulated convex optimization is solved with SALSA. Experiments provide evidence that LRSR is competitive with respect to the state-of-the-art methods.
关键词： Hyperspectral image super-resolution,low rank,superpixels
更新于2025-09-23 15:22:29
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Accelerated FRET-PAINT microscopy
摘要： Recent development of FRET-PAINT microscopy significantly improved the imaging speed of DNA-PAINT, the previously reported super-resolution fluorescence microscopy with no photobleaching problem. Here we try to achieve the ultimate speed limit of FRET-PAINT by optimizing the camera speed, dissociation rate of DNA probes, and bleed-through of the donor signal to the acceptor channel, and further increase the imaging speed of FRET-PAINT by 8-fold. Super-resolution imaging of COS-7 microtubules shows that high-quality 40-nm resolution images can be obtained in just tens of seconds.
关键词： FRET-PAINT,Super-resolution fluorescence microscopy,FRET,Single-molecule localization microscopy
更新于2025-09-23 15:22:29
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Separable-spectral convolution and inception network for hyperspectral image super-resolution
摘要： Due to the limitation of the imaging system, it is hard to get Hyperspectral Image (HSI) with very high spatial resolution. Super-Resolution (SR) is a handling missing data technology to restore high-frequency information from the low-resolution image, can be used to solve this problem. Recently, Deep Learning (DL) has achieved great performance in computer vision, including SR. However, most DL-based HSI SR methods neglect the spectral disorder caused by normal 2D convolution. This paper proposes a novel end–end deep learning-based network named Separable-Spectral and Inception Network (SSIN) for HSI SR. In SSIN, the feature extraction module independently extracts features of each band image, and then these features are fused together to further exploit residual image by using feature fusion module. In reconstruction module, a multi-path connection is built to obtain features of different levels to restore high spatial resolution image in a coarse-to-fine manner. Experiments are implemented on two datasets include both indoor and airborne HSIs, and the performances of SSIN are evaluated in different conditions. Experimental results show that adding several separable spectral convolutions and multi-path connection in a deep network can greatly improve the SR performance, and SSIN achieves higher accuracy and better visualization compare with other methods.
关键词： Hyperspectral Image,Separable-spectral convolution,Deep learning,Super-resolution,Multi-path reconstruction
更新于2025-09-23 15:22:29
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Infrared super-resolution imaging using multi-scale saliency and deep wavelet residuals
摘要： Infrared (IR) imaging systems with low-density focal plane arrays produce images with poor spatial resolution. To address this limitation, super-resolution (SR) algorithms can be applied on IR-low resolution (LR) images. In this paper, we present a new SR technique based on the multi-scale saliency detection and the residuals learned by the deep convolutional neural network (CNN) in the wavelet domain (DWCNN). The input LR image is processed in the transformed domain by applying 2D discrete wavelet transform. It decomposes an image into its low-frequency and high-frequency subbands. The multi-scale saliency detection is used to extract small scale and large scale salient feature maps from the bicubic upscaled LR image. These maps are incorporated in the high-frequency subbands of the LR image. Furthermore, the low-frequency and high-frequency subands are re?ned using the residuals learned by the DWCNN in training phase. The proposed algorithm is compared with the conventional and state-of-the-art SR methods. Results indicate that our method yields good reconstruction quality with high peak signal to ratio, structural similarity and low blur indices. Besides, our method requires less computational time.
关键词： Infrared imaging,Convolutional neural network,Discrete wavelet transform,Multi-scale saliency,Super-resolution
更新于2025-09-23 15:22:29
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Low-Dose Computed Tomography Image Super-Resolution Reconstruction via Random Forests
摘要： Aiming at reducing computed tomography (CT) scan radiation while ensuring CT image quality, a new low-dose CT super-resolution reconstruction method based on combining a random forest with coupled dictionary learning is proposed. The random forest classifier finds the optimal solution of the mapping relationship between low-dose CT (LDCT) images and high-dose CT (HDCT) images and then completes CT image reconstruction by coupled dictionary learning. An iterative method is developed to improve robustness, the important coefficients for the tree structure are discussed and the optimal solutions are reported. The proposed method is further compared with a traditional interpolation method. The results show that the proposed algorithm can obtain a higher peak signal-to-noise ratio (PSNR) and structural similarity index measurement (SSIM) and has better ability to reduce noise and artifacts. This method can be applied to many different medical imaging fields in the future and the addition of computer multithreaded computing can reduce time consumption.
关键词： super-resolution,coupled dictionary learning,random forests,low-dose CT
更新于2025-09-23 15:22:29
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Deep Distillation Recursive Network for Remote Sensing Imagery Super-Resolution
摘要： Deep convolutional neural networks (CNNs) have been widely used and achieved state-of-the-art performance in many image or video processing and analysis tasks. In particular, for image super-resolution (SR) processing, previous CNN-based methods have led to significant improvements, when compared with shallow learning-based methods. However, previous CNN-based algorithms with simple direct or skip connections are of poor performance when applied to remote sensing satellite images SR. In this study, a simple but effective CNN framework, namely deep distillation recursive network (DDRN), is presented for video satellite image SR. DDRN includes a group of ultra-dense residual blocks (UDB), a multi-scale purification unit (MSPU), and a reconstruction module. In particular, through the addition of rich interactive links in and between multiple-path units in each UDB, features extracted from multiple parallel convolution layers can be shared effectively. Compared with classical dense-connection-based models, DDRN possesses the following main properties. (1) DDRN contains more linking nodes with the same convolution layers. (2) A distillation and compensation mechanism, which performs feature distillation and compensation in different stages of the network, is also constructed. In particular, the high-frequency components lost during information propagation can be compensated in MSPU. (3) The final SR image can benefit from the feature maps extracted from UDB and the compensated components obtained from MSPU. Experiments on Kaggle Open Source Dataset and Jilin-1 video satellite images illustrate that DDRN outperforms the conventional CNN-based baselines and some state-of-the-art feature extraction approaches.
关键词： feature distillation,compensation unit,ultra-dense connection,super-resolution,video satellite,remote sensing imagery
更新于2025-09-23 15:22:29
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SMoLR: visualization and analysis of single-molecule localization microscopy data in R
摘要： Background: Single-molecule localization microscopy is a super-resolution microscopy technique that allows for nanoscale determination of the localization and organization of proteins in biological samples. For biological interpretation of the data it is essential to extract quantitative information from the super-resolution data sets. Due to the complexity and size of these data sets flexible and user-friendly software is required. Results: We developed SMoLR (Single Molecule Localization in R): a flexible framework that enables exploration and analysis of single-molecule localization data within the R programming environment. SMoLR is a package aimed at extracting, visualizing and analyzing quantitative information from localization data obtained by single-molecule microscopy. SMoLR is a platform not only to visualize nanoscale subcellular structures but additionally provides means to obtain statistical information about the distribution and localization of molecules within them. This can be done for individual images or SMoLR can be used to analyze a large set of super-resolution images at once. Additionally, we describe a method using SMoLR for image feature-based particle averaging, resulting in identification of common features among nanoscale structures. Conclusions: Embedded in the extensive R programming environment, SMoLR allows scientists to study the nanoscale organization of biomolecules in cells by extracting and visualizing quantitative information and hence provides insight in a wide-variety of different biological processes at the single-molecule level.
关键词： Image analysis,Image quantification,Super-resolution,Microscopy,R,Single-molecule localization
更新于2025-09-23 15:22:29
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Achieving Super-Resolution Remote Sensing Images via the Wavelet Transform Combined With the Recursive Res-Net
摘要： Deep learning (DL) has been successfully applied to single image super-resolution (SISR), which aims at reconstructing a high-resolution (HR) image from its low-resolution (LR) counterpart. Different from most current DL-based methods, which perform reconstruction in the spatial domain, we use a scheme based in the frequency domain to reconstruct the HR image at various frequency bands. Further, we propose a method that incorporates the wavelet transform (WT) and the recursive Res-Net. The WT is applied to the LR image to divide it into various frequency components. Then, an elaborately designed network with recursive residual blocks is used to predict high-frequency components. Finally, the reconstructed image is obtained via the inverse WT. This paper has three main contributions: 1) an SISR scheme based on the frequency domain is proposed under a DL framework to fully exploit the potential to depict images at different frequency bands; 2) recursive block and residual learning in global and local manners are adopted to ease the training of the deep network, and the batch normalization layer is removed to increase the flexibility of the network, save memory, and promote speed; and 3) the low-frequency wavelet component is replaced by an LR image with more details to further improve performance. To validate the effectiveness of the proposed method, extensive experiments are performed using the NWPU-RESISC45 data set, and the results demonstrate that the proposed method outperforms state-of-the-art methods in terms of both objective evaluation and subjective perspective.
关键词： residual learning,wavelet transform (WT),remote sensing image,super resolution,Recursive network
更新于2025-09-23 15:22:29