Bayesian Super-resolution Research Articles

Accelerating X-ray photoemission spectroscopy measurements using Bayesian super-resolution

This study applies Bayesian super-resolution to X-ray photoelectron spectroscopy (XPS), achieving up to a 20-fold reduction in measurement time while preserving data quality. Traditional XPS, crucial for surface analysis, typically requires extensive measurement durations. Our methodology significantly accelerates the process, as demonstrated with glass and Polytetrafluoroethylene samples, where we reduced measurement times by up to 1/20th without compromising spectral accuracy. This approach decreases noise levels and retains spectral integrity, offering a highly efficient solution for XPS. This innovation is particularly valuable in material science, enabling rapid, reliable surface analysis.

Channel Modeling and Estimation for Reconfigurable-Intelligent-Surface-Based 6G SAGIN IoT

The design of Internet of Thing (IoT) system over space-air-ground integrated network (SAGIN) is still in its infancy. It is critical to get 6G technology involved, in order to address the current problems in the combined SAGIN and IoT ecosystem. This work utilizes reconfigurable intelligent surface (RIS) to enhance the existing SAGIN infrastructure. We highlight channel modeling and estimate as a major component of RIS beam control and present a sparse Bayesian superresolution estimation approach to achieve a good balance of accuracy and computing complexity. Our numerical results justify that, with our proposed models and algorithms, RIS will become a cost-effective and spectral-efficient solution for SAGINbased 6G networks.

Application of Bayesian Super-Resolution to Spectroscopic Data for Precise Characterization of Spectral Peak Shape

The number of data points of digitally recorded spectra have been limited by the number of multichannel detectors employed, which sometimes impedes the precise characterization of spectral peak shape. Here we describe a methodology to increase the number of data points as well as the signal-to-noise (S/N) ratio by applying Bayesian super-resolution in the analysis of spectroscopic data. In our present method, first, the hyperparameters for the Bayesian super-resolution are determined by a virtual experiment imitating actual experimental data, and the precision of the super-resolution reconstruction is confirmed by the calculation of errors from the ideal values. For validation of the super-resolution reconstruction of spectroscopic data, we applied this method to the analysis of Raman spectra. From 200 Raman spectra of a reference Si substrate with a data interval of about 0.8 cm−1, super-resolution reconstruction with a data interval of 0.01 cm−1 was successfully achieved with the promised precision. From the super-resolution spectrum, the Raman scattering peak of the reference Si substrate was estimated as 520.55 (+0.12, −0.09) cm−1, which is comparable to the precisely determined value reported in previous works. The present methodology can be applied to various kinds of spectroscopic analysis, leading to increased precision in the analysis of spectroscopic data and the ability to detect slight differences in spectral peak positions and shapes.

A Novel Bayesian Super-Resolution Method for Radar Forward-Looking Imaging Based on Markov Random Field Model

Super-resolution technology is considered as an efficient approach to promote the image quality of forward-looking imaging radar. However, super-resolution technology is inherently an ill-conditioned issue, whose solution is quite susceptible to noise. Bayesian method can efficiently alleviate this issue through utilizing prior knowledge of the imaging process, in which the scene prior information plays a pretty significant role in ensuring the imaging accuracy. In this paper, we proposed a novel Bayesian super-resolution method on the basis of Markov random field (MRF) model. Compared with the traditional super-resolution method which is focused on one-dimensional (1-D) echo processing, the MRF model adopted in this study strives to exploit the two-dimensional (2-D) prior information of the scene. By using the MRF model, the 2-D spatial structural characteristics of the imaging scene can be well described and utilized by the nth-order neighborhood system. Then, the imaging objective function can be constructed through the maximum a posterior (MAP) framework. Finally, an accelerated iterative threshold/shrinkage method is utilized to cope with the objective function. Validation experiments using both synthetic echo and measured data are designed, and results demonstrate that the new MAP-MRF method exceeds other benchmarking approaches in terms of artifacts suppression and contour recovery.

A MAP-Based Approach for Hyperspectral Imagery Super-resolution.

In this study, we propose a novel single image Bayesian super-resolution (SR) algorithm where the hyperspectral image (HSI) is the only source of information. The main contribution of the proposed approach is to convert the ill-posed SR reconstruction (SRR) problem in the spectral domain to a quadratic optimization problem in the abundance map domain. In order to do so, Markov Random Field (MRF) based energy minimization approach is proposed and proved that the solution is quadratic. The proposed approach consists of five main steps. First, the number of endmembers in the scene is determined using virtual dimensionality. Second, the endmembers and their low resolution abundance maps are computed using simplex identification via the splitted augmented Lagrangian (SISAL) and fully constrained least squares (FCLS) algorithms. Third, high resolution (HR) abundance maps are obtained using our proposed maximum a posteriori (MAP) based energy function. This energy function is minimized subject to smoothness, unity and boundary constraints. Fourth, the HR abundance maps are further enhanced with texture preserving methods. Finally, HR HSI is reconstructed using the extracted endmembers and the enhanced abundance maps. The proposed method is tested on three real HSI datasets; namely the Cave, Harvard and Hyperspectral Remote Sensing Scenes (HRSS) and compared to state-of-the-art alternative methods using peak signal to noise ratio, structural similarity, spectral angle mapper and relative dimensionless global error in synthesis metrics. It is shown that the proposed method outperforms the state of the art methods in terms of quality while preserving the spectral consistency.

Computational methods in super-resolution microscopy

The broad applicability of super-resolution microscopy has been widely demonstrated in various areas and disciplines. The optimization and improvement of algorithms used in super-resolution microscopy are of great importance for achieving optimal quality of super-resolution imaging. In this review, we comprehensively discuss the computational methods in different types of super-resolution microscopy, including deconvolution microscopy, polarization-based super-resolution microscopy, structured illumination microscopy, image scanning microscopy, super-resolution optical fluctuation imaging microscopy, single-molecule localization microscopy, Bayesian super-resolution microscopy, stimulated emission depletion microscopy, and translation microscopy. The development of novel computational methods would greatly benefit super-resolution microscopy and lead to better resolution, improved accuracy, and faster image processing.

Super-resolving barcode images with an edge-preserving variational Bayesian framework

Limited resolution, blurring, warping, and additive noise associated with image acquisition and storage often make the barcode image degraded, generating low-resolution (LR) barcode images. Barcodes in degraded LR images are difficult to recognize. The goal of this paper is to introduce the potential of super-resolution (SR) technique in conquering the aforementioned challenges, and a variational Bayesian SR method is proposed in this work. Different from the previous work, here, the high-resolution barcode image is estimated through its corresponding posterior probability distribution. The barcode image is made of some homogeneous regions separated by sharp edges, and sometimes it is anisotropic. A universal prior probability distribution was proposed for the barcode image by considering these characteristics. Mathematically, the efficiency of this prior distribution is demonstrated, which can preserve sharp edges and suppress artifacts in the reconstructed barcode images. Moreover, by using the variational Bayesian framework, the motion parameters and hyperparameters can be estimated accurately and efficiently, ensuring the success of the SR technique. In order to overcome the difficulty caused by nonlinearity, the Taylor expansion method is introduced to solve the proposed SR problem. Eventually, the simulated and real data experiments show the encouraging performance of the proposed SR method. It increases certainly the reconstruction quality, and could be considerably robust against blur and noise. It is believed that the variational SR technique in the barcode auto-identification technique should open a further perspective of coping with technology challenge.

Simple, Low-Cost Bayesian Super Resolution Microscopy

Super-resolution Microscopy has made it possible to study sub-cellular structures below the resolution limit. Most super-resolution techniques, however, are costly and/or difficult to implement.We developed a low-cost super-resolution technique that allows us to observe details in the localization of rabbit polyclonal antibodies against KMP-11 protein on the membrane of Trypanosoma cruzi using equipment that is readily available in most biology labs. Also, we have confirmed the capability of our technique by imaging microtubules in Astrocytoma cell line. The technique uses an open-source algorithm based on Bayesian localization of blinking and bleaching of fluorescent molecules[1]. The process of bleaching and blinking is purely stochastic and common to all fluorescent probes[2]; therefore, the use of photoswitching dyes and specialized equipment such as EMCCDs or lasers is not necessary. Our implementation uses a standard fluorescence microscope with a mercury lamp, Alexa Fluor 594 and 488 as dyes and a standard scientific CCD.We have obtained super-resolution reconstructions of Trypanosoma cruzi with 70-nm FWHM for the cell membrane. The FWHM of microtubules was as low as 40-nm. These reconstructions exhibit much greater detail than the original fluorescence images.Our reconstructions are of inferior quality than those from other stochastic super-resolution techniques, however they can be achieved with a simple and accessible technique and standard laboratory instruments, making it suitable for both teaching and low-budget research labs.

A Variational Bayesian Superresolution Approach Using Adaptive Image Prior Model

The objective of superresolution is to reconstruct a high-resolution image by using the information of a set of low-resolution images. Recently, the variational Bayesian superresolution approach has been widely used. However, these methods cannot preserve edges well while removing noises. For this reason, we propose a new image prior model and establish a Bayesian superresolution reconstruction algorithm. In the proposed prior model, the degree of interaction between pixels is adjusted adaptively by an adaptive norm, which is derived based on the local image features. Moreover, in this paper, a monotonically decreasing function is used to calculate and update the single parameter, which is used to control the severity of penalizing image gradients in the proposed prior model. Thus, the proposed prior model is adaptive to the local image features thoroughly. With the proposed prior model, the edge details are preserved and noises are reduced simultaneously. A variational Bayesian inference is employed in this paper, and the formulas for calculating all the variables including the HR image, motion parameters, and hyperparameters are derived. These variables are refined progressively in an iterative manner. Experimental results show that the proposed SR approach is very efficient when compared to existing approaches.

A non-stationary image prior combination in super-resolution

A new Bayesian Super-Resolution (SR) image registration and reconstruction method is proposed. The new method utilizes a prior distribution based on a general combination of spatially adaptive, or non-stationary, image filters, which includes an adaptive local strength parameter able to preserve both image edges and textures. With the application of variational techniques, the proposed method allows for the automatic estimation of all problem unknowns. An experimental comparison between state of the art methods and the proposed SR approach has been performed on both synthetic and real images.

Light-sheet Bayesian microscopy enables deep-cell super-resolution imaging of heterochromatin in live human embryonic stem cells.

BackgroundHeterochromatin in the nucleus of human embryonic cells plays an important role in the epigenetic regulation of gene expression. The architecture of heterochromatin and its dynamic organization remain elusive because of the lack of fast and high-resolution deep-cell imaging tools. We enable this task by advancing instrumental and algorithmic implementation of the localization-based super-resolution technique.ResultsWe present light-sheet Bayesian super-resolution microscopy (LSBM). We adapt light-sheet illumination for super-resolution imaging by using a novel prism-coupled condenser design to illuminate a thin slice of the nucleus with high signal-to-noise ratio. Coupled with a Bayesian algorithm that resolves overlapping fluorophores from high-density areas, we show, for the first time, nanoscopic features of the heterochromatin structure in both fixed and live human embryonic stem cells. The enhanced temporal resolution allows capturing the dynamic change of heterochromatin with a lateral resolution of 50–60 nm on a time scale of 2.3 s.ConclusionLight-sheet Bayesian microscopy opens up broad new possibilities of probing nanometer-scale nuclear structures and real-time sub-cellular processes and other previously difficult-to-access intracellular regions of living cells at the single-molecule, and single cell level.

Kullback–Leibler Divergence Based Composite Prior Modeling for Bayesian Super-Resolution

This paper proposes to adaptively combine the known total variation model and more recent Frobenius norm regularization for multi-frame image super-resolution (SR). In contrast to existing literature, in this paper both the composite prior modeling and posterior variational optimization are achieved in the Bayesian framework by utilizing the Kullback---Leibler divergence, and hyper-parameters related to the composite prior and noise statistics are all determined automatically, resulting in a spatially adaptive SR reconstruction method. Experimental results demonstrate that the new approach can generate a super-resolved image with higher signal-to-noise ratio and better visual perception, not only image details better preserved but also staircase effects better suppressed.

A posterior mean approach for MRF-based spatially adaptive multi-frame image super-resolution

Multi-frame image super-resolution (SR) has been intensively studied in recent years, aiming at reconstructing high-resolution images from several degraded ones (e.g., shift, blurred, aliased, and noisy). In the literature, one of the most popular SR frameworks is the maximum a posteriori model, where a spatially homogeneous image prior and manually adjusted regularization parameter are commonly used for the entire high-resolution image, thus ignoring local spatially adaptive properties of natural images. In this paper, a posterior mean approach is proposed for spatially adaptive multi-frame image super-resolution. First, a flexible Laplacian prior is proposed incorporating both the gradient and Hessian information of images, not only able to better preserve image structures, e.g., edge, texture, but also to suppress staircase effects in the flat regions. In the subsequent, a fully Bayesian SR framework is formulated, wherein the variational Bayesian method is utilized to simultaneously estimate the high-resolution image and unknown hyper-parameters for the image prior and noise. The final experimental results show that the proposed approach is highly competitive against existing algorithms, producing a super-resolved image with higher peak signal-to-noise ratio and better visual perception.

Imaging heterochromatin in human embryonic stem cells with light-sheet Bayesian microscopy

We present a novel super-resolution microscope for the study of chromatin architecture. In eukaryotic cells, chromatin is organized into two distinct domains: lightly packed, and actively transcribed euchromatin, and highly condensed, and transcriptionally silent heterochromatin. While being transcriptionally repressive, heterochromatin is known to serve as binding sites for regulatory proteins, and therefore, plays an important role in the epigenetic regulation of gene expression [1]. However, despite its importance, the architecture of heterochromatin remains elusive, due to the lacking of powerful tools. Single-molecule super-resolution microscopy [2,3], invented by Betzig [4], Hess [5], and Zhuang [6], improves the spatial resolution of light microscopy by over an order of magnitude to ~20-30 nm, and therefore offers a new approach to examine the 3D architecture of chromatin. However, its fundamental requirement of high signal-to-noise ratio (SNR) of fluorescence signal to detect single fluorophore has limited its applications mainly to the study of the cytoskeleton and membrane proteins that reside near the surface of a cell [7-9]. To image beyond the surface of a cell where chromatin resides, we develop a Light-Sheet Bayesian Super-resolution Microscope (LSB-SRM). This microscope is a marriage between two recently developed techniques: light-sheet single molecule super-resolution microscopy and Bayesian super-resolution image reconstruction algorithm. The light-sheet condenser selective illuminates the nucleus of a cell, suppresses the fluorescence background from fluorophore in unwanted layer of the cell, and therefore increases the signal-to-noise ratio of the single-molecule detection. The Bayesian algorithm can efficiently resolve single molecules in images with noisy background. A combination of the two allows us to directly visualize heterochromatic structures in the nucleus of human embryonic stem cells at super-resolution. Our study shows that the heterochromatin organize into nanometer scale domain structures in H1, human embryonic stem cells, which is consistent with the topological domain theory derived from recent sequencing experiments [10,11].

Bayesian combination of sparse and non-sparse priors in image super resolution

In this paper the application of image prior combinations to the Bayesian Super Resolution (SR) image registration and reconstruction problem is studied. Two sparse image priors, a Total Variation (TV) prior and a prior based on the @?1 norm of horizontal and vertical first-order differences (f.o.d.), are combined with a non-sparse Simultaneous Auto Regressive (SAR) prior. Since, for a given observation model, each prior produces a different posterior distribution of the underlying High Resolution (HR) image, the use of variational approximation will produce as many posterior approximations as priors we want to combine. A unique approximation is obtained here by finding the distribution on the HR image given the observations that minimizes a linear convex combination of Kullback-Leibler (KL) divergences. We find this distribution in closed form. The estimated HR images are compared with the ones obtained by other SR reconstruction methods.

Application of Bayesian image superresolution to spectral reflectance estimation

Hyperspectral imaging provides us with high-dimensional, scene-independent color information but faces problems such as long image acquisition time and severe lighting and focusing conditions. To achieve efficient spectral imaging, this study presents an extended method of Bayesian image superresolution. The proposed method increases both the spatial and the wavelength resolution of input images and enables the processing of hyperspectral images to a higher resolution from easily acquired low-spatial-resolution multispectral images. In an experiment using acquired multispectral images of a Japanese traditional painting, visible and near-infrared hyperspectral images were produced, and the obvious effect of superresolution was validated.

Generative Bayesian Image Super Resolution With Natural Image Prior

We propose a new single image super resolution (SR) algorithm via Bayesian modeling with a natural image prior modeled by a high-order Markov random field (MRF). SR is one of the long-standing and active topics in image processing community. It is of great use in many practical applications, such as astronomical observation, medical imaging, and the adaptation of low-resolution contents onto high-resolution displays. One category of the conventional approaches for image SR is formulating the problem with Bayesian modeling techniques and then obtaining its maximum-a-posteriori solution, which actually boils down to a regularized regression task. Although straightforward, this approach cannot exploit the full potential offered by the probabilistic modeling, as only the posterior mode is sought. On the other hand, current Bayesian SR approaches using the posterior mean estimation typically use very simple prior models for natural images to ensure the computational tractability. In this paper, we present a Bayesian image SR approach with a flexible high-order MRF model as the prior for natural images. The minimum mean square error (MMSE) criteria are used for estimating the HR image. A Markov chain Monte Carlo-based sampling algorithm is presented for obtaining the MMSE solution. The proposed method cannot only enjoy the benefits offered by the flexible prior, but also has the advantage of making use of the probabilistic modeling to perform a posterior mean estimation, thus is less sensitive to the local minima problem as the MAP solution. Experimental results indicate that the proposed method can generate competitive or better results than state-of-the-art SR algorithms.

Subsurface imaging for anti-personal mine detection by Bayesian super-resolution with a smooth-gap prior

Ground-penetrating radars (GPRs) have been studied in order to reconstruct subsurface images. The signal observed by GPR typically includes very strong noise, and reconstruction of the image is a difficult task. We propose a new subsurface imaging method based on the framework of Bayesian super-resolution. In the framework, we can incorporate additional information into the reconstructed image by considering a smooth-gap prior, which can represent the smoothness of the subsurface image and gaps between materials, and which improves the quality of the reconstructed image. We investigated the performance of the proposed method with a synthetic GPR dataset, and confirmed the validity of the proposed method.

Robust super resolution of compressed video

This paper presents a robust algorithm to recover high-frequency information from compressed low-resolution (LR) video sequences. Previous super-resolution (SR) approaches have succeeded in resolution enhancement when the motion in the LR sequence is simple. However, when the motion is complex, new artifacts will be introduced in the SR processing. To solve this problem, we develop a robust Bayesian SR algorithm with two steps. We first isolate the frames individually to get their corresponding initial SR solutions within the Bayesian framework. Secondly, with a robust cost function to reject outliers and noise, final SR images are achieved with multiple LR frames. In the mean time, we impose the constraint that the distribution of high-resolution (HR) image gradient should be equal to one of the corresponding decompressed LR images to sharpen the edges of the results. As a result of these steps, we are able to produce high-quality deblurred results, which show a suppressing of high-frequency artifacts and less ringing artifacts, with a higher peak signal-to-noise ratio (PSNR).

Variational Bayesian Super Resolution

In this paper, we address the super resolution (SR) problem from a set of degraded low resolution (LR) images to obtain a high resolution (HR) image. Accurate estimation of the sub-pixel motion between the LR images significantly affects the performance of the reconstructed HR image. In this paper, we propose novel super resolution methods where the HR image and the motion parameters are estimated simultaneously. Utilizing a bayesian formulation, we model the unknown HR image, the acquisition process, the motion parameters and the unknown model parameters in a stochastic sense. Employing a variational bayesian analysis, we develop two novel algorithms which jointly estimate the distributions of all unknowns. The proposed framework has the following advantages: 1) Through the incorporation of uncertainty of the estimates, the algorithms prevent the propagation of errors between the estimates of the various unknowns; 2) the algorithms are robust to errors in the estimation of the motion parameters; and 3) using a fully bayesian formulation, the developed algorithms simultaneously estimate all algorithmic parameters along with the HR image and motion parameters, and therefore they are fully-automated and do not require parameter tuning. We also show that the proposed motion estimation method is a stochastic generalization of the classical Lucas-Kanade registration algorithm. Experimental results demonstrate that the proposed approaches are very effective and compare favorably to state-of-the-art SR algorithms.