Residual Feature Attentional Fusion Network for Lightweight Chest CT Image Super-Resolution

The diagnosis of COVID-19 requires chest computed tomography (CT). High-resolution CT images can provide more diagnostic information to help doctors better diagnose the disease, so it is of clinical importance to study super-resolution (SR) algorithms applied to CT images to improve the reso-lution of CT images. However, most of the existing SR algorithms are studied based on natural images, which are not suitable for medical images;and most of these algorithms improve the reconstruction quality by increasing the network depth, which is not suitable for machines with limited resources. To alleviate these issues, we propose a residual feature attentional fusion network for lightweight chest CT image super-resolution (RFAFN). Specifically, we design a contextual feature extraction block (CFEB) that can extract CT image features more efficiently and accurately than ordinary residual blocks. In addition, we propose a feature-weighted cascading strategy (FWCS) based on attentional feature fusion blocks (AFFB) to utilize the high-frequency detail information extracted by CFEB as much as possible via selectively fusing adjacent level feature information. Finally, we suggest a global hierarchical feature fusion strategy (GHFFS), which can utilize the hierarchical features more effectively than dense concatenation by progressively aggregating the feature information at various levels. Numerous experiments show that our method performs better than most of the state-of-the-art (SOTA) methods on the COVID-19 chest CT dataset. In detail, the peak signal-to-noise ratio (PSNR) is 0.11 dB and 0.47 dB higher on CTtest1 and CTtest2 at x3 SR compared to the suboptimal method, but the number of parameters and multi-adds are reduced by 22K and 0.43G, respectively. Our method can better recover chest CT image quality with fewer computational resources and effectively assist in COVID-19.

A Novel Method to Map Small RNAs with High Resolution.

Analyzing cellular structures and the relative location of molecules is essential for addressing biological questions. Super-resolution microscopy techniques that bypass the light diffraction limit have become increasingly popular to study cellular molecule dynamics in situ. However, the application of super-resolution imaging techniques to detect small RNAs (sRNAs) is limited by the choice of proper fluorophores, autofluorescence of samples, and failure to multiplex. Here, we describe an sRNA-PAINT protocol for the detection of sRNAs at nanometer resolution. The method combines the specificity of locked nucleic acid probes and the low background, precise quantitation, and multiplexable characteristics of DNA Point Accumulation for Imaging in Nanoscale Topography (DNA-PAINT). Using this method, we successfully located sRNA targets that are important for development in maize anthers at sub-20 nm resolution and quantitated their exact copy numbers. Graphic abstract: Multiplexed sRNA-PAINT. Multiple Vetting and Analysis of RNA for In Situ Hybridization (VARNISH) probes with different docking strands (i.e., a, b, …) will be hybridized to samples. The first probe will be imaged with the a* imager. The a* imager will be washed off with buffer C, and then the sample will be imaged with b* imager. The wash and image steps can be repeated sequentially for multiplexing.

Simultaneous Super-Resolution and Classification of Lung Disease Scans.

Acute lower respiratory infection is a leading cause of death in developing countries. Hence, progress has been made for early detection and treatment. There is still a need for improved diagnostic and therapeutic strategies, particularly in resource-limited settings. Chest X-ray and computed tomography (CT) have the potential to serve as effective screening tools for lower respiratory infections, but the use of artificial intelligence (AI) in these areas is limited. To address this gap, we present a computer-aided diagnostic system for chest X-ray and CT images of several common pulmonary diseases, including COVID-19, viral pneumonia, bacterial pneumonia, tuberculosis, lung opacity, and various types of carcinoma. The proposed system depends on super-resolution (SR) techniques to enhance image details. Deep learning (DL) techniques are used for both SR reconstruction and classification, with the InceptionResNetv2 model used as a feature extractor in conjunction with a multi-class support vector machine (MCSVM) classifier. In this paper, we compare the proposed model performance to those of other classification models, such as Resnet101 and Inceptionv3, and evaluate the effectiveness of using both softmax and MCSVM classifiers. The proposed system was tested on three publicly available datasets of CT and X-ray images and it achieved a classification accuracy of 98.028% using a combination of SR and InceptionResNetv2. Overall, our system has the potential to serve as a valuable screening tool for lower respiratory disorders and assist clinicians in interpreting chest X-ray and CT images. In resource-limited settings, it can also provide a valuable diagnostic support.

Twinned Residual Auto-Encoder (TRAE)-A new DL architecture for denoising super-resolution and task-aware feature learning from COVID-19 CT images.

The detection of the COronaVIrus Disease 2019 (COVID-19) from Computed Tomography (CT) scans has become a very important task in modern medical diagnosis. Unfortunately, typical resolutions of state-of-the-art CT scans are still not adequate for reliable and accurate automatic detection of COVID-19 disease. Motivated by this consideration, in this paper, we propose a novel architecture that jointly affords the Single-Image Super-Resolution (SISR) and the reliable classification problems from Low Resolution (LR) and noisy CT scans. Specifically, the proposed architecture is based on a couple of Twinned Residual Auto-Encoders (TRAE), which exploits the feature vectors and the SR images recovered by a Master AE for performing transfer learning and then improves the training of a "twinned" Follower AE. In addition, we also develop a Task-Aware (TA) version of the basic TRAE architecture, namely the TA-TRAE, which further utilizes the set of feature vectors generated by the Follower AE for the joint training of an additional auxiliary classifier, so to perform automated medical diagnosis on the basis of the available LR input images without human support. Experimental results and comparisons with a number of state-of-the-art CNN/GAN/CycleGAN benchmark SISR architectures, performed by considering × 2 , × 4 , and × 8 super-resolution (i.e., upscaling) factors, support the effectiveness of the proposed TRAE/TA-TRAE architectures. In particular, the detection accuracy attained by the proposed architectures outperforms the corresponding ones of the implemented CNN, GAN and CycleGAN baselines up to 9.0%, 6.5%, and 6.0% at upscaling factors as high as × 8 .

Image Scanning Microscopy to Investigate Polycomb Protein Colocalization onto Chromatin

Super-resolution microscopy has been recently applied to understand the 3D topology of chromatin at an intermediated genomic scale (kilobases to a few megabases), as this corresponds to a sub-diffraction spatial scale crucial for the regulation of gene transcription. In this context, polycomb proteins are very renowned gene repressors that organize into the multiprotein complexes Polycomb Repressor Complex 1 (PRC1) and 2 (PRC2). PRC1 and PRC2 operate onto the chromatin according to a complex mechanism, which was recently recapitulated into a working model. Here, we present a functional colocalization study at 100–140 nm spatial resolution targeting PRC1 and PRC2 as well as the histone mark H3K27me3 by Image Scanning Microscopy (ISM). ISM offers a more flexible alternative to diffraction-unlimited SRMs such as STORM and STED, and it is perfectly suited to investigate the mesoscale of PRC assembly. Our data suggest a partially simultaneous effort of PRC1 and PRC2 in locally shaping the chromatin topology.

JDSR-GAN: Constructing An Efficient Joint Learning Network for Masked Face Super-Resolution

With the growing importance of preventing the COVID-19 virus in cyber-manufacturing security, face images obtained in most video surveillance scenarios are usually low resolution together with mask occlusion. However, most of the previous face super-resolution solutions can not efficiently handle both tasks in one model. In this work, we consider both tasks simultaneously and construct an efficient joint learning network, called JDSR-GAN, for masked face super-resolution tasks. Given a low-quality face image with mask as input, the role of the generator composed of a denoising module and super-resolution module is to acquire a high-quality high-resolution face image. The discriminator utilizes some carefully designed loss functions to ensure the quality of the recovered face images. Moreover, we incorporate the identity information and attention mechanism into our network for feasible correlated feature expression and informative feature learning. By jointly performing denoising and face super-resolution, the two tasks can complement each other and attain promising performance. Extensive qualitative and quantitative results show the superiority of our proposed JDSR-GAN over some competitive methods. IEEE

A Concept-Based Review on Generative Adversarial Network for Generating Super Resolution Medical Image Using SWOT Analysis

Several alarming health challenges are urging medical experts and practitioners to research and develop new approaches to diagnose, detect and control the early spread of deadly diseases. One of the most challenging is Coronavirus Infection (Covid-19). Models have been proposed to detect and diagnose early infection of the virus to attain proper precautions against the Covid-19 virus. However, some researchers adopt parameter optimization to attain better accuracy on the Chest X-ray images of covid-19 and other related diseases. Hence, this research work adopts a hybridized cascaded feature extraction technique (Local Binary Pattern LBP and Histogram of Oriented Gradients HOG) and Convolutional Neural Network CNN for the deep learning classification model. The merging of LBP and HOG feature extraction significantly improved the performance level of the deep-learning CNN classifier. As a result, 95% accuracy, 92% precision, and 93% recall are attained by the proposed model. © 2022 IEEE.

Coronaviruses Use ACE2 Monomers as Entry-Receptors.

The angiotensin-converting enzyme 2 (ACE2) has been identified as entry receptor on cells enabling binding and infection with the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) via trimeric spike (S) proteins protruding from the viral surface. It has been suggested that trimeric S proteins preferably bind to plasma membrane areas with high concentrations of possibly multimeric ACE2 receptors to achieve a higher binding and infection efficiency. Here we used direct stochastic optical reconstruction microscopy (dSTORM) in combination with different labeling approaches to visualize the distribution and quantify the expression of ACE2 on different cells. Our results reveal that endogenous ACE2 receptors are present as monomers in the plasma membrane with densities of only 1-2 receptors µm-2 . In addition, binding of trimeric S proteins does not induce the formation of ACE2 oligomers in the plasma membrane. Supported by infection studies using vesicular stomatitis virus (VSV) particles bearing S proteins our data demonstrate that a single S protein interaction per virus particle with a monomeric ACE2 receptor is sufficient for infection, which provides SARS-CoV-2 a high infectivity.

SIM imaging resolves endocytosis of SARS-CoV-2 spike RBD in living cells.

It is urgent to understand the infection mechanism of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) for the prevention and treatment of COVID-19. The infection of SARS-CoV-2 starts when the receptor-binding domain (RBD) of viral spike protein binds to angiotensin-converting enzyme 2 (ACE2) of the host cell, but the endocytosis details after this binding are not clear. Here, RBD and ACE2 were genetically coded and labeled with organic dyes to track RBD endocytosis in living cells. The photostable dyes enable long-term structured illumination microscopy (SIM) imaging and to quantify RBD-ACE2 binding (RAB) by the intensity ratio of RBD/ACE2 fluorescence. We resolved RAB endocytosis in living cells, including RBD-ACE2 recognition, cofactor-regulated membrane internalization, RAB-bearing vesicle formation and transport, RAB degradation, and downregulation of ACE2. The RAB was found to activate the RBD internalization. After vesicles were transported and matured within cells, RAB was finally degraded after being taken up by lysosomes. This strategy is a promising tool to understand the infection mechanism of SARS-CoV-2.

Fast Image Super-Resolution Using Particle Swarm Optimization-Based Convolutional Neural Networks.

Image super-resolution based on convolutional neural networks (CNN) is a hot topic in image processing. However, image super-resolution faces significant challenges in practical applications. Improving its performance on lightweight architectures is important for real-time super-resolution. In this paper, a joint algorithm consisting of modified particle swarm optimization (SMCPSO) and fast super-resolution convolutional neural networks (FSRCNN) is proposed. In addition, a mutation mechanism for particle swarm optimization (PSO) was obtained. Specifically, the SMCPSO algorithm was introduced to optimize the weights and bias of the CNNs, and the aggregation degree of the particles was adjusted adaptively by a mutation mechanism to ensure the global searching ability of the particles and the diversity of the population. The results showed that SMCPSO-FSRCNN achieved the most significant improvement, being about 4.84% better than the FSRCNN model, using the BSD100 data set at a scale factor of 2. In addition, a chest X-ray super-resolution images classification test experiment was conducted, and the experimental results demonstrated that the reconstruction ability of this model could improve the classification accuracy by 13.46%; in particular, the precision and recall rate of COVID-19 were improved by 45.3% and 6.92%, respectively.

Mapping of functional SARS-CoV-2 receptors in human lungs establishes differences in variant binding and SLC1A5 as a viral entry modulator of hACE2.

BACKGROUND: The COVID-19 pandemic is an infectious disease caused by SARS-CoV-2. The first step of SARS-CoV-2 infection is the recognition of angiotensin-converting enzyme 2 (ACE2) receptors by the receptor-binding domain (RBD) of the viral Spike (S) glycoprotein. Although the molecular and structural bases of the SARS-CoV-2-RBD/hACE2 interaction have been thoroughly investigated in vitro, the relationship between hACE2 expression and in vivo infection is less understood. METHODS: Here, we developed an efficient SARS-CoV-2-RBD binding assay suitable for super resolution microscopy and simultaneous hACE2 immunodetection and mapped the correlation between hACE2 receptor abundance and SARS-CoV-2-RBD binding, both in vitro and in human lung biopsies. Next, we explored the specific proteome of SARS-CoV-2-RBD/hACE2 through a comparative mass spectrometry approach. FINDINGS: We found that only a minority of hACE2 positive spots are actually SARS-CoV-2-RBD binding sites, and that the relationship between SARS-CoV-2-RBD binding and hACE2 presence is variable, suggesting the existence of additional factors. Indeed, we found several interactors that are involved in receptor localization and viral entry and characterized one of them: SLC1A5, an amino acid transporter. High-resolution receptor-binding studies showed that co-expression of membrane-bound SLC1A5 with hACE2 predicted SARS-CoV-2 binding and entry better than hACE2 expression alone. SLC1A5 depletion reduces SARS-CoV-2 binding and entry. Notably, the Omicron variant is more efficient in binding hACE2 sites, but equally sensitive to SLC1A5 downregulation. INTERPRETATION: We propose a method for mapping functional SARS-CoV-2 receptors in vivo. We confirm the existence of hACE2 co-factors that may contribute to differential sensitivity of cells to infection. FUNDING: This work was supported by an unrestricted grant from "Fondazione Romeo ed Enrica Invernizzi" to Stefano Biffo and by AIRC under MFAG 2021 - ID. 26178 project - P.I. Manfrini Nicola.

A novel fuzzy hierarchical fusion attention convolution neural network for medical image super-resolution reconstruction

The clarity of medical images is crucial for doctors to identify and diagnose different diseases. High-resolution images have more detailed information and clearer content than low-resolution images. It is well known that medical images can frequently have some blurred object boundaries, and that traditional deep learning models cannot adequately describe the uncertainty of these blurred boundaries. This paper proposes a new fuzzy metric to characterize the uncertainty of pixels and designs a fuzzy hierarchical fusion attention neural network based on multiscale guided learning. Specifically, a fuzzy neural information-processing block is proposed, which converts an input image into a fuzzy domain using fuzzy membership functions. The uncertainty of the pixels is processed using the proposed fuzzy rules, and then the output of the fuzzy rule layer is fused with the result of the convolution in the neural network. Simultaneously, a multiscale guided-learning dense residual block and pyramidal hierarchical attention module are designed to extract more effective hierarchical image information. Finally, a recurrent memory module with a residual structure is used to process the output features of the hierarchical attention modules. A recursive sub-pixel reconstruction module is used at the tail of the network to reconstruct the images. Compared with existing super-resolution methods using the public COVID-CT dataset, the proposed method demonstrated superior performance in high-resolution medical image reconstruction and reduced the number of parameters and analysis time of the models.

Artificial intelligence-assisted multistrategy image enhancement of chest X-rays for COVID-19 classification.

Background: The coronavirus disease 2019 (COVID-19) led to a dramatic increase in the number of cases of patients with pneumonia worldwide. In this study, we aimed to develop an AI-assisted multistrategy image enhancement technique for chest X-ray (CXR) images to improve the accuracy of COVID-19 classification. Methods: Our new classification strategy consisted of 3 parts. First, the improved U-Net model with a variational encoder segmented the lung region in the CXR images processed by histogram equalization. Second, the residual net (ResNet) model with multidilated-rate convolution layers was used to suppress the bone signals in the 217 lung-only CXR images. A total of 80% of the available data were allocated for training and validation. The other 20% of the remaining data were used for testing. The enhanced CXR images containing only soft tissue information were obtained. Third, the neural network model with a residual cascade was used for the super-resolution reconstruction of low-resolution bone-suppressed CXR images. The training and testing data consisted of 1,200 and 100 CXR images, respectively. To evaluate the new strategy, improved visual geometry group (VGG)-16 and ResNet-18 models were used for the COVID-19 classification task of 2,767 CXR images. The accuracy of the multistrategy enhanced CXR images was verified through comparative experiments with various enhancement images. In terms of quantitative verification, 8-fold cross-validation was performed on the bone suppression model. In terms of evaluating the COVID-19 classification, the CXR images obtained by the improved method were used to train 2 classification models. Results: Compared with other methods, the CXR images obtained based on the proposed model had better performance in the metrics of peak signal-to-noise ratio and root mean square error. The super-resolution CXR images of bone suppression obtained based on the neural network model were also anatomically close to the real CXR images. Compared with the initial CXR images, the classification accuracy rates of the internal and external testing data on the VGG-16 model increased by 5.09% and 12.81%, respectively, while the values increased by 3.51% and 18.20%, respectively, for the ResNet-18 model. The numerical results were better than those of the single-enhancement, double-enhancement, and no-enhancement CXR images. Conclusions: The multistrategy enhanced CXR images can help to classify COVID-19 more accurately than the other existing methods.

Technologies Enabling Single-Molecule Super-Resolution Imaging of mRNA.

The transient nature of RNA has rendered it one of the more difficult biological targets for imaging. This difficulty stems both from the physical properties of RNA as well as the temporal constraints associated therewith. These concerns are further complicated by the difficulty in imaging endogenous RNA within a cell that has been transfected with a target sequence. These concerns, combined with traditional concerns associated with super-resolution light microscopy has made the imaging of this critical target difficult. Recent advances have provided researchers the tools to image endogenous RNA in live cells at both the cellular and single-molecule level. Here, we review techniques used for labeling and imaging RNA with special emphases on various labeling methods and a virtual 3D super-resolution imaging technique.

Super-Resolution-Empowered Adaptive Medical Video Streaming in Telemedicine Systems

Due to influence of COVID-19, telemedicine is becoming more and more important. High-quality medical videos can provide a physician with a better visual experience and increase the accuracy of disease diagnosis, but this requires a dramatic increase in bandwidth compared to that required by regular videos. Existing adaptive video-streaming approaches cannot successfully provide high-resolution video-streaming services under poor or fluctuating network conditions with limited bandwidth. In this paper, we propose a super-resolution-empowered adaptive medical video streaming in telemedicine system (named SR-Telemedicine) to provide high quality of experience (QoE) videos for the physician while saving the network bandwidth. In SR-Telemedicine, very low-resolution video chunks are first transmitted from the patient to an edge computing node near the physician. Then, a video super-resolution (VSR) model is employed at the edge to reconstruct the low-resolution video chunks into high-resolution ones with an appropriate high-resolution level (such as 720p or 1080p). Furthermore, the neural network of VSR model is designed to be scalable and can be determined dynamically. Based on the time-varying computational capability of the edge computing node and the network condition, a double deep Q-Network (DDQN)-based algorithm is proposed to jointly select the optimal reconstructed high-resolution level and the scale of the VSR model. Finally, extensive experiments based on real-world traces are carried out, and the experimental results illustrate that the proposed SR-Telemedicine system can improve the QoE of medical videos by 17–79% compared to three baseline algorithms. © 2022 by the authors.

Inter-User Distance Estimation Based on a New Type of Fingerprint in Massive MIMO System for COVID-19 Contact Detection.

In this paper, we address the challenging task of estimating the distance between different users in a Millimeter Wave (mmWave) massive Multiple-Input Multiple-Output (mMIMO) system. The conventional Time of Arrival (ToA) and Angle of Arrival (AoA) based methods need users under the Line-of-Sight (LoS) scenario. Under the Non-LoS (NLoS) scenario, the fingerprint-based method can extract the fingerprint that includes the location information of users from the channel state information (CSI). However, high accuracy CSI estimation involves a huge overhead and high computational complexity. Thus, we design a new type of fingerprint generated by beam sweeping. In other words, we do not have to know the CSI to generate fingerprint. In general, each user can record the Received Signal Strength Indicator (RSSI) of the received beams by performing beam sweeping. Such measured RSSI values, formatted in a matrix, could be seen as beam energy image containing the angle and location information. However, we do not use the beam energy image as the fingerprint directly. Instead, we use the difference between two beam energy images as the fingerprint to train a Deep Neural Network (DNN) that learns the relationship between the fingerprints and the distance between these two users. Because the proposed fingerprint is rich in terms of the users' location information, the DNN can easily learn the relationship between the difference between two beam energy images and the distance between those two users. We term it as the DNN-based inter-user distance (IUD) estimation method. Nonetheless, we investigate the possibility of using a super-resolution network to reduce the involved beam sweeping overhead. Using super-resolution to increase the resolution of low-resolution beam energy images obtained by the wide beam sweeping for IUD estimation can facilitate considerate improvement in accuracy performance. We evaluate the proposed DNN-based IUD estimation method by using original images of resolution 4 × 4, 8 × 8, and 16 × 16. Simulation results show that our method can achieve an average distance estimation error equal to 0.13 m for a coverage area of 60 × 30 m2. Moreover, our method outperforms the state-of-the-art IUD estimation methods that rely on users' location information.

Residual Dense Attention Networks for COVID-19 Computed Tomography Images Super-Resolution

The coronavirus disease 2019 (COVID-19) is highly infectious and pathogenic and poses a severe threat to people’s public safety. Owing to the low-resolution of computed tomography (CT) images, it is essential to use super-resolution reconstruction technology to improve the resolution of COVID-19 medical images. Aiming at the problems of the limited receptive field, low resolution, high complexity, and loss of edge information in the super-resolution reconstruction method of residual learning, we present a residual dense attention network (RDAN) for COVID-19 CT image super-resolution. Firstly, to better extract features and reduce the number of parameters, we design the residual dense network module to extract detailed information from images. Secondly, we add the channel attention mechanism to enable the network to have adequate high-frequency information with larger weights to reduce the computational cost of the model. Finally, we filter and reorganize the multi-layer image information by skip connections so that the network model can allow extensive use of image information of different depths. Comprehensive benchmark evaluation shows that our RDAN method dramatically improves the convergence speed of the network, solves the problem of missing information, and makes the reconstructed COVID-19 CT images have more apparent textures, richer details, and better visual effects that can effectively assist experts in diagnosis. IEEE

Imaging Techniques: Essential Tools for the Study of SARS-CoV-2 Infection.

The world has seen the emergence of a new virus in 2019, SARS-CoV-2, causing the COVID-19 pandemic and millions of deaths worldwide. Microscopy can be much more informative than conventional detection methods such as RT-PCR. This review aims to present the up-to-date microscopy observations in patients, the in vitro studies of the virus and viral proteins and their interaction with their host, discuss the microscopy techniques for detection and study of SARS-CoV-2, and summarize the reagents used for SARS-CoV-2 detection. From basic fluorescence microscopy to high resolution techniques and combined technologies, this article shows the power and the potential of microscopy techniques, especially in the field of virology.

Localization-Based Super-Resolution Microscopy Reveals Relationship between SARS-CoV2 Spike and Phosphatidylinositol (4,5)-bisphosphate.

Localization microscopy circumvents the diffraction limit by identifying and measuring the positions of numerous subsets of individual fluorescent molecules, ultimately producing an image whose resolution depends on the uncertainty and density of localization, and whose capabilities are compatible with imaging living specimens. Spectral resolution can be improved by incorporating a dichroic or dispersive element in the detection path of a localization microscope, which can be useful for separation of multiple probes imaged simultaneously and for detection of changes in emission spectra of fluorophores resulting from changes in their environment. These methodological advances enable new biological applications, which in turn motivate new questions and technical innovations. As examples, we present fixed-cell imaging of the spike protein SARS-CoV2 (S) and its interactions with host cell components. Results show a relationship between S and the lipid phosphatidylinositol (4,5)-bisphosphate (PIP2). These findings have ramifications for several existing models of plasma membrane organization.

Super-resolution wavelets for recovery of arbitrarily close point-masses with arbitrarily small coefficients

Three families of super-resolution (SR) wavelets Ψv,ng(x), Ψu,n,ms(x) and Ψw,n,mds(x), to be called Gaussian SR (GSR), spline SR (SSR) and dual-spline SR (DSSR) wavelets, respectively, are introduced in this paper for resolving the super-resolution problem of recovering any point-mass h(y)=∑ℓ=1Lcℓδ(y−σℓ), with ;σℓ−σk;≥η for ℓ≠k, σℓ≠0, and ;cℓ;>η⁎ for all ℓ,k=1,…,L, where η>0 and η⁎>0 are allowed to be arbitrarily small. Let Ψα,n=Ψv,ng, Ψu,n,ms or Ψw,n,mds, with α=v,u or w, respectively, where m=12,1,2,⋯ is suppressed. The SR wavelets are designed to have the n-th order of vanishing moments, with Fourier transform of their complex conjugates Ψ¯ˆα,n(x) to possess the following properties: (i) Ψ¯ˆα,n(x)≥0 for all x∈R, (ii) maxx⁡Ψ¯ˆα,n(x)=Ψ¯ˆα,n(κ)=ξn, where κ≐2.331122371 and ξ≐1.449222080 are positive constants independent of n,α and m, and (iii) the widths (or standard deviations) of Ψ¯ˆα,n(x), with center at κ, tends to 0 very fast for large values of α. While the most popular approach to resolve this super-resolution problem is to consider the Fourier transform d(x)=∑ℓ=1Lcℓe−iσℓx of h(y) as the “data function” for solving the inverse problem of recovering L, σ1,⋯,σL and c1,⋯,cL of the point-mass d(x), our proposed approach is to consider the “enhanced data function” D(a;α,n):=FΨα,n(a)=∑ℓ=1LcℓΨ¯ˆα,n(aσℓ), where FΨα,n(a), to be called the search function in this paper, is obtained by taking the continuous wavelet transform (CWT): (WΨα,nd)(t,a)=∫−∞∞Ψα,n(y−ta)‾d(y)dya of the data function d(x), with Ψα,n as the analysis wavelet, followed by applying wavelet thresholding to “de-noise” the data function d(x), by choosing an appropriate thresholding parameter γ>0, with γ<η⁎×ξn, in order not to remove any of the coefficients cℓ, where ℓ=1,⋯,L;and finally by setting t=0. Hence, the enhanced data function D(a;α,n) is at least cleaner than the data function d(x). In our proposed approach, instead of directly recovering σ1,⋯,σL as in the published literature, we propose a “divide and conquer” strategy: first by applying “bottom-up thresholding” of the search function FΨα,n(a), with thresholding parameter γ⁎>0 close to but not exceeding η⁎×ξn, to separate the set of the local extrema locations aℓ:=κσℓ of the function FΨα,n(a) in {a∈R:a≠0} into disjoint intervals of clusters, with more and smaller intervals and less number of local extrema aℓ in each interval for larger values of α;and secondly, by applying “top-down thresholding” to extract, one-by-one, of all local maxima, followed by all local minima (after a sign change), for each and every cluster. A desired leeway Δ>0 and lower bounds of the choice of the width parameter α are derived for the iterative application of top-down thresholding. Extension to Rs for s≥2 is also studied in this paper. For s=2, we observe that the imagery of the enhanced data function for a single point-mass at (σ1,σ2) where σ1,σ2≠0, resembles that of an “Airy disk” with center at (κ/σ1,κ/σ2) in light microscopy and celestial telescopy of point-masses.