Super-resolution Approach Research Articles

Medical image super-resolution: A review, recent trends and techniques

The quality and resolution of medical images significantly impact the accuracy of clinical decisions. Preprocessing of medical images plays a crucial role in the performance of computer-aided diagnosis (CAD) systems, which helps to improve the quality of input images, remove noise, and enhance contrast. Medical images are often characterized by low resolution due to different constraints such as hardware limits and image acquisition time. To address this issue, researchers have proposed various super-resolution approaches to improve the resolution of medical imagesand therefore better image quality. Hence, the super-resolution (SR) process aims to reconstruct a high-resolution (HR) image from its low-resolution (LR) counterpart. Several techniques are suggested in the literature including interpolation-based methods, reconstruction-based methods and learning-based methods. In recent years, deep learning-based methods have shownpromising results in various medical image applications while preserving important features and structures. In this article, we present a review of different studies that have proven to be useful in medical image super-resolution including classical computer vision methods and deep learning based-methods.

Super-Resolution of Dental Panoramic Radiographs Using Deep Learning: A Pilot Study.

Using super-resolution (SR) algorithms, an image with a low resolution can be converted into a high-quality image. Our objective was to compare deep learning-based SR models to a conventional approach for improving the resolution of dental panoramic radiographs. A total of 888 dental panoramic radiographs were obtained. Our study involved five state-of-the-art deep learning-based SR approaches, including SR convolutional neural networks (SRCNN), SR generative adversarial network (SRGAN), U-Net, Swin for image restoration (SwinIr), and local texture estimator (LTE). Their results were compared with one another and with conventional bicubic interpolation. The performance of each model was evaluated using the metrics of mean squared error (MSE), peak signal-to-noise ratio (PNSR), structural similarity index (SSIM), and mean opinion score by four experts (MOS). Among all the models evaluated, the LTE model presented the highest performance, with MSE, SSIM, PSNR, and MOS results of 7.42 ± 0.44, 39.74 ± 0.17, 0.919 ± 0.003, and 3.59 ± 0.54, respectively. Additionally, compared with low-resolution images, the output of all the used approaches showed significant improvements in MOS evaluation. A significant enhancement in the quality of panoramic radiographs can be achieved by SR. The LTE model outperformed the other models.

A new approach for super-resolution and classification applications on neonatal thermal images

ABSTRACT Thermal imaging systems are harmless to human health and enable contactless heat measurements. The thermal cameras are used in many public sectors where it is necessary to detect the change of temperature values. However, thermal cameras are costly and produce images with low edge information. This situation prevents the widespread use of thermal cameras. Therefore, in recent years, the researches to advance the quality of thermal images have increased. Within the scope of the studies in this paper, first of all, three different datasets consisting of thermal images in the colourful format of neonates were created. Also, TSRGAN+ deep network model was presented for super-resolution studies. The super-resolution images obtained visually approached ground truth images to a great extent. In addition, these results were compared using the peak signal to noise ratio (PSNR) and the structural similarity index measure (SSIM) image quality metrics. The proposed model showed a superior success in terms of the values of PSNR and SSIM compared to the state-of-the-art models. Here, the PSNR value of the proposed TSRGAN+ model increased by 1–1.5 dB compared to the TSRGAN network architecture, while the SSIM value increased by around 2–3%. Finally, the unhealthy-healthy image classification applications were performed on all thermal image sets in order to implement both the task-based evaluation and a real-life application. Thus, a new method is presented to evaluate the results of the super-resolution studies. Here, firstly, a CNN-based classifier was designed and the classification metrics were obtained for all three datasets. Then, transfer learning was applied using state-of-the-art models (ResNet101, Xception) to increase classification success. Here, the most successful results were obtained in applications using the ResNet101 model. Also, the developed model outperformed the TSRGAN, which achieved the second-best result. When all the obtained results are evaluated, it has been observed that the super-resolution models increase the success of unhealthy-healthy classification by about 10% compared to the low resolution images. In other words, the effects of super-resolution techniques on classification applications are clearly seen. In summary, the logical use of the super-resolution research will enable the common use of low-cost thermal cameras in the application fields such as medicine.

Lightweight Feature De-redundancy and Self-calibration Network for Efficient Image Super-resolution

In recent years, thanks to the inherent powerful feature representation and learning abilities of the convolutional neural network (CNN), deep CNN-steered single image super-resolution approaches have achieved remarkable performance improvements. However, these methods are often accompanied by large consumption of computing and memory resources, which is difficult to be adopted in real-world application scenes. To handle this issue, we design an efficient Feature De-redundancy and Self-calibration Super-resolution network (FDSCSR). In particular, a Feature De-redundancy and Self-calibration Block (FDSCB) is proposed to reduce the repetitive feature information extracted by the model and further enhance the efficiency of the model. Then, based on FDSCB, a Local Feature Fusion Module is presented to elaborately utilize and fuse the feature information extracted by each FDSCB. Abundant experiments on benchmarks have demonstrated that our FDSCSR achieves superior performance with relatively less computational consumption and storage resource than other state-of-the-art approaches. The code is available at https://github.com/IVIPLab/FDSCSR .

A survey of image super-resolution algorithms based on convolutional neural networks for smart city health care applications

Image super-resolution (SR) is a technique for improving an image by increasing its spatial resolution. A super-resolution approach can improve an image's pixel intensity to the next optimized level. The SR schemes have a challenging task in several applications like remote sensing, medical imaging, and biological detection. The SR technique is divided into single-image SR (SISR) and multi-image SR (MISR) based on the number of input images (MISR). This work reviews SISR with diverse image datasets like Urban 100, Set5, Set4, and DIV2k. Convolution neural networks (CNN) are considered one of the boosting solutions to implement the superresolution and contribute to remarkable progress. This survey deals with the review of diverse convolution neural networkbased SR techniques used in various applications. The purpose of this proposed survey is to investigate several convolution neural network-based SR approaches that are employed in various applications. This study analyses the characteristics of various convolution neural network-based image super-resolution algorithms in terms of peak signal to noise ratio (PSNR) and structural similarity index measure (SSIM). According to the survey, the 3D dilated convolutional encoder-decoder network is the best qualitative & quantitative analysis method for brain MRI super-resolution.

Reference-Based Super-Resolution Method for Remote Sensing Images with Feature Compression Module

High-quality remote sensing images play important roles in the development of ecological indicators’ mapping, urban-rural management, urban planning, and other fields. Compared with natural images, remote sensing images have more abundant land cover along with lower spatial resolutions. Given the embedded longitude and latitude information of remote sensing images, reference (Ref) images with similar scenes could be more accessible. However, existing traditional super-resolution (SR) approaches always depend on increases in network depth to improve performance, which limits the acquisition and application of high-quality remote sensing images. In this paper, we proposed a novel, reference-image-based, super-resolution method with feature compression module (FCSR) for remote sensing images to alleviate the above issue while effectively utilizing high-resolution (HR) information from Ref images. Specifically, we exploited a feature compression branch (FCB) to extract relevant features in feature detail matching with large measurements. This branch employed a feature compression module (FCM) to extract features from low-resolution (LR) and Ref images, which enabled texture transfer from different perspectives. To decrease the impact of environmental factors such as resolution, brightness and ambiguity disparities between the LR and Ref images, we designed a feature extraction encoder (FEE) to ensure accuracy in feature extraction in the feature acquisition branch. The experimental results demonstrate that the proposed FCSR achieves significant performance and visual quality compared to state-of-the-art SR methods. Explicitly, when compared with the best method, the average peak signal-to-noise ratio (PSNR) index on the three test sets is improved by 1.0877%, 0.8161%, 1.0296%, respectively, and the structural similarity (SSIM) index on four test sets is improved by 1.4764%, 1.4467%, 0.0882%, and 1.8371%, respectively. Simultaneously, FCSR obtains satisfactory visual details following qualitative evaluation.

A super-resolution proof of concept in a cycloidal coded aperture miniature mass spectrometer.

Higher resolution in fieldable mass spectrometers (MS) is desirable in space flight applications to enable resolving isobaric interferences at m/z < 60 u. Resolution in portable cycloidal MS coupled with array detectors could be improved by reducing the slit width and/or by reducing the width of the detector pixels. However, these solutions are expensive and can result in reduced sensitivity. In this paper, we demonstrate high-resolution spectral reconstruction in a cycloidal coded aperture miniature mass spectrometer (C-CAMMS) without changing the slit or detector pixel sizes using a class of signal processing techniques called super-resolution (SR). We developed an SR reconstruction algorithm using a sampling SR approach whereby a set of spatially shifted low-resolution measurements are reconstructed into a higher-resolution spectrum. This algorithm was applied to experimental data collected using the C-CAMMS prototype. It was then applied to synthetic data with additive noise, system response variation, and spatial shift nonuniformity to investigate the source of reconstruction artifacts in the experimental data. Experimental results using two ½ pixel shifted spectra resulted in a resolution of ¾ pixel full width at half maximum (FWHM) at m/z = 28 u. This resolution is equivalent to 0.013 u, six times better than the resolution previously published at m/z = 28 for N2 + using C-CAMMS. However, the reconstructed spectra exhibited some artifacts. The results of the synthetic data study indicate that the artifacts are most likely caused by the system response variation. This paper demonstrates super-resolution spectral reconstruction in C-CAMMS without changing the slit or detector pixel sizes using a sampling SR approach. With improvements, this technique could be used to resolve isobaric interferences in a portable cycloidal MS for space flight applications.

Super-Resolution Mass Spectrometry Enables Rapid, Accurate, and Highly Multiplexed Proteomics at the MS2 Level.

In tandem mass spectrometry (MS2)-based multiplexed quantitative proteomics, the complement reporter ion approaches (TMTc and TMTproC) were developed to eliminate the ratio-compression problem of conventional MS2-level approaches. Resolving all high m/z complement reporter ions (∼6.32 mDa-spaced) requires mass resolution and scan speeds above the performance levels of OrbitrapTM instruments. Therefore, complement reporter ion quantification with TMT/TMTpro reagents is currently limited to 5 out of 11 (TMT) or 9 out of 18 (TMTpro) channels (∼1 Da spaced). We first demonstrate that a FusionTM LumosTM Orbitrap can resolve 6.32 mDa-spaced complement reporter ions with standard acquisition modes extended with 3 s transients. We then implemented a super-resolution mass spectrometry approach using the least-squares fitting (LSF) method for processing Orbitrap transients to achieve shotgun proteomics-compatible scan rates. The LSF performance resolves the 6.32 mDa doublets for all TMTproC channels in the standard mass range with transients as short as ∼108 ms (Orbitrap resolution setting of 50,000 at m/z 200). However, we observe a slight decrease in measurement precision compared to 1 Da spacing with the 108 ms transients. With 256 ms transients (resolution of 120,000 at m/z 200), coefficients of variation are essentially indistinguishable from 1 Da samples. We thus demonstrate the feasibility of highly multiplexed, accurate, and precise shotgun proteomics at the MS2 level.

Bayesian Image Super-Resolution With Deep Modeling of Image Statistics.

Modeling statistics of image priors is useful for image super-resolution, but little attention has been paid from the massive works of deep learning-based methods. In this work, we propose a Bayesian image restoration framework, where natural image statistics are modeled with the combination of smoothness and sparsity priors. Concretely, first we consider an ideal image as the sum of a smoothness component and a sparsity residual, and model real image degradation including blurring, downscaling, and noise corruption. Then, we develop a variational Bayesian approach to infer their posteriors. Finally, we implement the variational approach for single image super-resolution (SISR) using deep neural networks, and propose an unsupervised training strategy. The experiments on three image restoration tasks, i.e., ideal SISR, realistic SISR, and real-world SISR, demonstrate that our method has superior model generalizability against varying noise levels and degradation kernels and is effective in unsupervised SISR. The code and resulting models are released via https://zmiclab.github.io/projects.html.

An Arbitrary Scale Super-Resolution Approach for 3D MR Images via Implicit Neural Representation

High Resolution (HR) medical images provide rich anatomical structure details to facilitate early and accurate diagnosis. In magnetic resonance imaging (MRI), restricted by hardware capacity, scan time, and patient cooperation ability, isotropic 3-dimensional (3D) HR image acquisition typically requests long scan time and, results in small spatial coverage and low signal-to-noise ratio (SNR). Recent studies showed that, with deep convolutional neural networks, isotropic HR MR images could be recovered from low-resolution (LR) input via single image super-resolution (SISR) algorithms. However, most existing SISR methods tend to approach scale-specific projection between LR and HR images, thus these methods can only deal with fixed up-sampling rates. In this paper, we propose ArSSR, an Arbitrary Scale Super-Resolution approach for recovering 3D HR MR images. In the ArSSR model, the LR image and the HR image are represented using the same implicit neural voxel function with different sampling rates. Due to the continuity of the learned implicit function, a single ArSSR model is able to achieve arbitrary and infinite up-sampling rate reconstructions of HR images from any input LR image. Then the SR task is converted to approach the implicit voxel function via deep neural networks from a set of paired HR and LR training examples. The ArSSR model consists of an encoder network and a decoder network. Specifically, the convolutional encoder network is to extract feature maps from the LR input images and the fully-connected decoder network is to approximate the implicit voxel function. Experimental results on three datasets show that the ArSSR model can achieve state-of-the-art SR performance for 3D HR MR image reconstruction while using a single trained model to achieve arbitrary up-sampling scales.

Multicolor super-resolution imaging to study human coronavirus RNA during cellular infection.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the third human coronavirus within 20 years that gave rise to a life-threatening disease and the first to reach pandemic spread. While the scientific community has studied coronavirus biology using genomics, cryoelectron microscopy, and electron tomography, how coronavirus RNA is spatially organized in the cell at the different stages of the viral replication cycle at nanoscale resolution is largely unknown. To make therapeutic headway against current and future coronaviruses, the biology of coronavirus RNA during infection must be precisely understood. Here, we introduce a multicolor super-resolution (SR) fluorescence imaging framework to examine the spatial interactions between viral RNA and other viral factors during host cell infection. We demonstrate the efficacy of our approach using the HCoV-229E coronavirus in MRC5 lung fibroblasts and specifically label two key oligonucleotide viral players: viral genomic RNA (gRNA) and double-stranded RNA (dsRNA). The 10-nm resolution achieved by our approach uncovers a striking spatial organization of gRNA and dsRNA into three distinct RNA structures: (1) large gRNA clusters, (2) very tiny nanoscale gRNA puncta containing a single copy of the genome, and (3) round intermediate-sized puncta highlighted by the dsRNA label. Furthermore, we use our two-color SR approach to visualize the nanoscale spatial relationships between viral gRNA and the endoplasmic reticulum (ER), dsRNA and ER, gRNA and the spike protein, and gRNA and dsRNA. In particular, we observe two striking observations that provide insight into viral replication and export. First, spike proteins and gRNA rarely assemble into an assembled virion in the MRC5 cytoplasm. Second, in contrast to previous observations, dsRNA and gRNA spatially separate. Our approach provides a comprehensive imaging framework that will enable future investigations of coronavirus fundamental biology and the effects of therapeutics.

Super-resolution microscopy and studies of peroxisomes.

Fluorescence microscopy is an important tool forstudying cellular structures such as organelles. Unfortunately, many details in the corresponding images are hidden due to the resolution limit of conventional lens-based far-field microscopy. An example is the study of peroxisomes, where important processes such as molecular organization during protein important can simply not be studied with conventional far-field microscopy methods. A remedy is super-resolution fluorescence microscopy, which is nowadays a well-established technique for the investigation of inner-cellular structures but has so far to a lesser extent been applied to the study of peroxisomes. To help advancing the latter, we here give an overview over the different super-resolution microscopy approaches and their potentials and challenges in cell-biological research, including labelling issues and a focus on studies on peroxisomes. Here, we alsohighlight experiments beyond simple imaging such as observations of diffusion dynamics of peroxisomal proteins.

A bidirectional semantic segmentation method for remote sensing image based on super-resolution and domain adaptation

ABSTRACT Image semantic segmentation methods based on convolutional neural networks rely on supervised learning with labels, and their performance often drops significantly when applied to unlabelled datasets from different sources. The domain adaptation methods can reduce the inconsistency of feature distribution between the unlabelled target domain data used for testing and the labelled source domain data used for training, thus improve the segmentation performance and have more practical applications. However, in the field of remote sensing image processing, if the spatial resolutions of the source domain and the target domain are different and this problem is not to be solved, the performance of the transferred model will be affected. In this paper, we propose a bidirectional semantic segmentation method based on super-resolution and domain adaption (BSSM-SRDA), which is suitable for the transfer learning task of a semantic segmentation model from a low-resolution source domain data to a high-resolution target domain data. BSSM-SRDA mainly consists of three parts: a shared feature extraction network; a super-resolution image translation module, which incorporates a super-resolution approach to reduce spatial resolution differences and visual style differences of the two domains; a domain-adaptive semantic segmentation module, which combines an adversarial domain adaptation approach to reduce differences at the output level. At the same time, we design a new bidirectional self-supervised learning algorithm for BSSM-SRDA that facilitates mutually beneficial learning of the super-resolution image translation module and the domain-adaptive semantic segmentation module. The experiments demonstrate the superiority of the proposed method over other state-of-the-art methods on two remote-sensing image datasets, with mIoU improvements of 2.5% and 3.2%, respectively. Code: https://github.com/mageliang/BSSM-SRDA.git

MGluR5 is transiently confined in perisynaptic nanodomains to shape synaptic function

The unique perisynaptic distribution of postsynaptic metabotropic glutamate receptors (mGluRs) at excitatory synapses is predicted to directly shape synaptic function, but mechanistic insight into how this distribution is regulated and impacts synaptic signaling is lacking. We used live-cell and super-resolution imaging approaches, and developed molecular tools to resolve and acutely manipulate the dynamic nanoscale distribution of mGluR5. Here we show that mGluR5 is dynamically organized in perisynaptic nanodomains that localize close to, but not in the synapse. The C-terminal domain of mGluR5 critically controlled perisynaptic confinement and prevented synaptic entry. We developed an inducible interaction system to overcome synaptic exclusion of mGluR5 and investigate the impact on synaptic function. We found that mGluR5 recruitment to the synapse acutely increased synaptic calcium responses. Altogether, we propose that transient confinement of mGluR5 in perisynaptic nanodomains allows flexible modulation of synaptic function.

A Novel Multi-Task Learning Network Based on Melanoma Segmentation and Classification with Skin Lesion Images.

Melanoma is known worldwide as a malignant tumor and the fastest-growing skin cancer type. It is a very life-threatening disease with a high mortality rate. Automatic melanoma detection improves the early detection of the disease and the survival rate. In accordance with this purpose, we presented a multi-task learning approach based on melanoma recognition with dermoscopy images. Firstly, an effective pre-processing approach based on max pooling, contrast, and shape filters is used to eliminate hair details and to perform image enhancement operations. Next, the lesion region was segmented with a VGGNet model-based FCN Layer architecture using enhanced images. Later, a cropping process was performed for the detected lesions. Then, the cropped images were converted to the input size of the classifier model using the very deep super-resolution neural network approach, and the decrease in image resolution was minimized. Finally, a deep learning network approach based on pre-trained convolutional neural networks was developed for melanoma classification. We used the International Skin Imaging Collaboration, a publicly available dermoscopic skin lesion dataset in experimental studies. While the performance measures of accuracy, specificity, precision, and sensitivity, obtained for segmentation of the lesion region, were produced at rates of 96.99%, 92.53%, 97.65%, and 98.41%, respectively, the performance measures achieved rates for classification of 97.73%, 99.83%, 99.83%, and 95.67%, respectively.

Application of deep learning-based super-resolution to T1-weighted postcontrast gradient echo imaging of the chest

ObjectivesA deep learning-based super-resolution for postcontrast volume-interpolated breath-hold examination (VIBE) of the chest was investigated in this study. Aim was to improve image quality, noise, artifacts and diagnostic confidence without change of acquisition parameters.Materials and methodsFifty patients who received VIBE postcontrast imaging of the chest at 1.5 T were included in this retrospective study. After acquisition of the standard VIBE (VIBES), a novel deep learning-based algorithm and a denoising algorithm were applied, resulting in enhanced images (VIBEDL). Two radiologists qualitatively evaluated both datasets independently, rating sharpness of soft tissue, vessels, bronchial structures, lymph nodes, artifacts, cardiac motion artifacts, noise levels and overall diagnostic confidence, using a Likert scale ranging from 1 to 4. In the presence of lung lesions, the largest lesion was rated regarding sharpness and diagnostic confidence using the same Likert scale as mentioned above. Additionally, the largest diameter of the lesion was measured.ResultsThe sharpness of soft tissue, vessels, bronchial structures and lymph nodes as well as the diagnostic confidence, the extent of artifacts, the extent of cardiac motion artifacts and noise levels were rated superior in VIBEDL (all P < 0.001).There was no significant difference in the diameter or the localization of the largest lung lesion in VIBEDL compared to VIBES. Lesion sharpness as well as detectability was rated significantly better by both readers with VIBEDL (both P < 0.001).ConclusionThe application of a novel deep learning-based super-resolution approach in T1-weighted VIBE postcontrast imaging resulted in an improvement in image quality, noise levels and diagnostic confidence as well as in a shortened acquisition time.

(Retracted) Super-resolution generative adversarial networks using autoencoder reduce dimension

Generative adversarial network (GAN) has been widely applied to produce super-resolution (SR) image with real perception and texture details. In most existing SR approaches, the training objective typically measures a pixel-wise average distance between the SR and high-resolution (HR) images. However, as the degradation function of different images from HR to low resolution (LR) is generally different, optimizing such metrics often leads to certain unpleasant artificial traces. Unlike the prevalent GAN inversion methods that require expensive image-specific optimization at runtime, we present an alternative formulation by directly leveraging latent representation produced by a pretrained AutoEncoder. We call this improved method reduce dimension super-resolved GAN (RD-SRGAN). RD-SRGAN first obtains the latent feature representation of LR image by a pretrained AutoEncoder as input to the generator network. This process not only reduces noise effects but also decreases the overall computational complexity. On the other hand, the residual between the ground truth and the produced images replaces the produced images as input to the discriminator network, and a 2D zero mean Gaussian noise with controllable low variance replaces the real images as another input to the discriminator network. By leveraging the feature representation and properties of the 2D zero mean Gaussian noise, we restrict the optimization space to produce an SR image. Therefore, the residual of the generated SR images tend to approximates to a Gaussian noise, which introduces useless deviation information as little as possible. Experimental results show that RD-SRGAN can benefit from these strategies and achieve improved fidelity and naturalness comparison to existing methods. Switching the pretrained AutoEncoder allows the method to deal with images from diverse categories, e.g., remote sensing satellite imaging, medical imaging, and astronomy.

TnTViT-G: Transformer in Transformer Network for Guidance Super Resolution

Image Super Resolution is a potential approach that can improve the image quality of low-resolution optical sensors, leading to improved performance in various industrial applications. It is important to emphasize that most state-of-the-art super resolution algorithms often use a single channel of input data for training and inference. However, this practice ignores the fact that the cost of acquiring high-resolution images in various spectral domains can differ a lot from one another. In this paper, we attempt to exploit complementary information from a low-cost channel (visible image) to increase the image quality of an expensive channel (infrared image). We propose a dual stream Transformer-based super resolution approach that uses the visible image as a guide to super-resolve another spectral band image. To this end, we introduce Transformer in Transformer network for Guidance super resolution, named TnTViT-G, an efficient and effective method that extracts the features of input images via different streams and fuses them together at various stages. In addition, unlike other guidance super resolution approaches, TnTViT-G is not limited to a fixed upsample size and it can generate super-resolved images of any size. Extensive experiments on various datasets show that the proposed model outperforms other state-of-the-art super resolution approaches. TnTViT-G surpasses state-of-the-art methods by up to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$0.19 \sim 2.3dB $ </tex-math></inline-formula> , while it is memory efficient.

On the well-posedness of a tensor-based second order PDE with bilateral term for image super-resolution

&lt;p style='text-indent:20px;'&gt;In this paper, we introduce a second-order equation based on a diffusion tensor combined with a bilateral total variation (BTV) term that takes the benefit from the diffusion model of Perona-Malik in the homogeneous regions, the Weickert model near sharp edge and the BTV term in reducing blur. Compared to the other partial differential equations (PDE) used in the SR context, the proposed super-resolution (SR) PDE can efficiently preserve image components such as corners and flat regions with less apparition of blur in homogeneous regions. Moreover, since the SR approaches are always ill-posed, a mathematical study of the existence and uniqueness of the solution is also checked in a Sobolev space. From the numerical experiments we can observe that the proposed PDE can efficiently improve the quality of the high-resolution (HR) image. Surprisingly, the apparition of blur in the restored image is less compared to the other methods. In addition to visual evaluation, two-based metrics are computed to confirm the improvements offered by the proposed PDE.&lt;/p&gt;

Metrics of 2D immunological synapses in human T cells via high-content confocal cell imaging.

Immunological synapses (IS) are the privileged site of complex information transfer between T cells and antigen presenting cells. IS are highly structured in terms of actin and tubulin cytoskeleton organization, receptor and proximal signal patterning, and intracellular organelle polarization. The magnitude and quality of T cell responses upon antigen recognition is dependent on IS molecular organization. For that reason, methods to precisely assess IS parameters are crucial to monitor T cell activation and function in health and disease, but also for T cell centered therapeutic intervention. Confocal and super-resolution microscopy approaches have allowed to characterize the complex structure of the T cell IS. However, those approaches suffer from a low-throughput and low-content format precluding multi-parametric classification of IS across large numbers of samples or stimulatory conditions. Here, we present a protocol of high-content confocal cell imaging in a 384-well plate format adapted to the unbiased analysis of primary T cells forming IS over pre-coated stimulatory molecules. The protocol focuses on the staining of F-actin, pericentrin and granzyme B in CD8+ T cells, but is transposable to other IS molecular markers and lymphocyte subsets. We discuss potential applications offered by the multi-parametric characterization of T cell IS in a high-throughput format.