FLASH Imaging Research Articles

Contrast-preserving image smoothing via the truncated first-order rational function

The main task of image smoothing is to remove the insignificant details of the input image while preserving salient structural edges. In the fields of computer vision and graphics, image smoothing techniques are of great practical importance. In this paper, we investigate a new nonconvex variational optimization model for contrast-preserving image smoothing based on the truncated first-order rational (TFOR) penalty function. We employ an iterative numerical method that utilizes the half-quadratic minimization to effectively solve the proposed model. To validate the effectiveness of the proposed method, we compare it with some related state-of-the-art methods. Experimental results are given to show that the proposed method performs well in preserving the image contrast while maintaining the important edges and structures. We apply the proposed method on various classic image processing tasks such as clip-art compression artifact removal, detail enhancement, image denoising, image abstraction, flash and no-flash image restoration, and guided depth map upsampling.

Fixational Eye Movements Enhance the Precision of Visual Information Transmitted by the Primate Retina.

Fixational eye movements alter the number and timing of spikes transmitted from the retina to the brain, but whether these changes enhance or degrade the retinal signal is unclear. To quantify this, we developed a Bayesian method for reconstructing natural images from the recorded spikes of hundreds of retinal ganglion cells (RGCs) in the macaque retina (male), combining a likelihood model for RGC light responses with the natural image prior implicitly embedded in an artificial neural network optimized for denoising. The method matched or surpassed the performance of previous reconstruction algorithms, and provides an interpretable framework for characterizing the retinal signal. Reconstructions were improved with artificial stimulus jitter that emulated fixational eye movements, even when the eye movement trajectory was assumed to be unknown and had to be inferred from retinal spikes. Reconstructions were degraded by small artificial perturbations of spike times, revealing more precise temporal encoding than suggested by previous studies. Finally, reconstructions were substantially degraded when derived from a model that ignored cell-to-cell interactions, indicating the importance of stimulus-evoked correlations. Thus, fixational eye movements enhance the precision of the retinal representation.

Robust Reflection Removal With Flash-Only Cues in the Wild.

We propose a simple yet effective reflection-free cue for robust reflection removal from a pair of flash and ambient (no-flash) images. The reflection-free cue exploits a flash-only image obtained by subtracting the ambient image from the corresponding flash image in raw data space. The flash-only image is equivalent to an image taken in a dark environment with only a flash on. This flash-only image is visually reflection-free and thus can provide robust cues to infer the reflection in the ambient image. Since the flash-only image usually has artifacts, we further propose a dedicated model that not only utilizes the reflection-free cue but also avoids introducing artifacts, which helps accurately estimate reflection and transmission. Our experiments on real-world images with various types of reflection demonstrate the effectiveness of our model with reflection-free flash-only cues: our model outperforms state-of-the-art reflection removal approaches by more than 5.23dB in PSNR. We extend our approach to handheld photography to address the misalignment between the flash and no-flash pair. With misaligned training data and the alignment module, our aligned model outperforms our previous version by more than 3.19dB in PSNR on a misaligned dataset. We also study using linear RGB images as training data. Our source code and dataset are publicly available at https://github.com/ChenyangLEI/flash-reflection-removal.

BSCSML: Design of an Efficient Bioinspired Security &Privacy Model for Cyber Physical System using Machine Learning

With the increasing prevalence of Smart Grid Cyber Physical Systems with Advanced Metering Infrastructure (SG CPS AMI), securing their internal components has become one of the paramount concerns. Traditional security mechanisms have proven to be insufficient in defending against sophisticated attacks. Bioinspired security and privacy models have emerged as promising solutions due to their stochastic solutions. This paper proposes a novel bio-inspired security and privacy model for SG CPS AMI that utilizes machine learning to strengthen their security levels. The proposed model is inspired by the hybrid Grey Wolf Teacher Learner based Optimizer (GWTLbO) Method’s ability to detect and respond to threats in real-time deployments. The GWTLbO Model also ensures higher privacy by selecting optimal methods between k-privacy, t-closeness & l-diversity depending upon contextual requirements. This study improves system accuracy and efficiency under diverse attacks using machine learning techniques. The method uses supervised learning to teach the model to recognize known attack trends and uncontrolled learning to spot unknown attacks. Our model was tested using real-time IoT device data samples. The model identified Zero-Day Attacks, Meter Bypass, Flash Image Manipulation, and Buffer-level attacks. The proposed model detects and responds to attacks with high accuracy and low false-positive rates. In real-time operations, the proposed model can handle huge volumes of data efficiently. The bioinspired security and privacy model secures CPS efficiently and is scalable for various cases. Machine learning techniques can improve the security and secrecy of these systems and revolutionize defense against different attacks.

Detection of Endometriosis Lesions Using Gd-Based Collagen I Targeting Probe in Murine Models of Endometriosis

PurposeEndometriosis is a chronic condition characterized by high fibrotic content and affecting about 10% of women during their reproductive years. Yet, no clinically approved agents are available for non-invasive endometriosis detection. The purpose of this study was to investigate the utility of a gadolinium-based collagen type I targeting probe (EP-3533) to non-invasively detect endometriotic lesions using magnetic resonance imaging (MRI). Previously, this probe has been used for detection and staging of fibrotic lesions in the liver, lung, heart, and cancer. In this study we evaluate the potential of EP-3533 for detecting endometriosis in two murine models and compare it with a non-binding isomer (EP-3612).ProceduresFor imaging, we utilized two GFP-expressing murine models of endometriosis (suture model and injection model) injected intravenously with EP3533 or EP-33612. Mice were imaged before and after bolus injection of the probes. The dynamic signal enhancement of MR T1 FLASH images was analyzed, normalized, and quantified, and the relative location of lesions was validated through ex vivo fluorescence imaging. Subsequently, the harvested lesions were stained for collagen, and their gadolinium content was quantified by inductively coupled plasma optical emission spectrometry (ICP-OES).ResultsWe showed that EP-3533 probe increased the signal intensity in T1-weighted images of endometriotic lesions in both models of endometriosis. Such enhancement was not detected in the muscles of the same groups or in endometriotic lesions of mice injected with EP-3612 probe. Consequentially, control tissues had significantly lower gadolinium content, compared to the lesions in experimental groups. Probe accumulation was similar in endometriotic lesions of either model.ConclusionsThis study provides evidence for feasibility of targeting collagen type I in the endometriotic lesions using EP3533 probe. Our future work includes investigation of the utility of this probe for therapeutic delivery in endometriosis to inhibit signaling pathways that cause the disease.

Generative Adversarial Network based method for generation of synthetic image parameters for TACTIC [formula omitted]-ray telescope

Very High Energy (VHE) γ and cosmic rays interact with the atmosphere and generate an Extensive Air Shower (EAS) of particles comprising mainly electron–positron pairs. These energetic secondary charged particles move down the atmosphere and upon de-excitation, the medium generates a flash of Cherenkov light. The subtle differences in the shape of Cherenkov flash images generated by γ/cosmic rays at the top of the atmosphere can be explored by the ground-based Imaging Atmospheric Cherenkov technique (IACT) telescopes. To characterize and calibrate such IACT telescopes, an extensive library of EAS for γ-rays and cosmic rays of various energies are required. However, the main difficulty in generating a simulation database is that the process of shower generation is computationally extensive. To overcome the challenge of creating a huge database of simulated shower images, we propose a method based on the generation of synthetic data by employing the unsupervised machine learning (ML)-based method, namely the Generative Adversarial Networks (GAN). The present study discusses the methodology for generating synthetic image parameters using GAN for the TACTIC telescope. A comparison between Monte Carlo simulation-generated image parameters for the TACTIC and GAN-generated parameters is presented. Results are also presented from the independent Random Forest-based ML classifier to identify similarities between the simulation and synthetic data species. The potential applications of GAN-generated data towards IACT data analysis for TACTIC are also discussed.

Robust Image Denoising of No-Flash Images Guided by Consistent Flash Images

Images taken in low light conditions typically contain distracting noise, and eliminating such noise is a crucial computer vision problem. Additional photos captured with a camera flash can guide an image denoiser to preserve edges since the flash images often contain fine details with reduced noise. Nonetheless, a denoiser can be misled by inconsistent flash images, which have image structures (e.g., edges) that do not exist in no-flash images. Unfortunately, this disparity frequently occurs as the flash/no-flash pairs are taken in different light conditions. We propose a learning-based technique that robustly fuses the image pairs while considering their inconsistency. Our framework infers consistent flash image patches locally, which have similar image structures with the ground truth, and denoises no-flash images using the inferred ones via a combination model. We demonstrate that our technique can produce more robust results than state-of-the-art methods, given various flash/no-flash pairs with inconsistent image structures. The source code is available at https://github.com/CGLab-GIST/RIDFnF.

Substantia nigra integrity is associated with poorer recognition memory for novel stimuli in early Alzheimer’s disease

AbstractBackgroundThe substantia nigra/ventral tegmental area (SN/VTA), the brain’s most prominent dopamine source, is involved in novelty exploration and episodic memory. Using a novelty detection task combined with neuromelanin‐sensitive MRI, this work aimed to investigate the relationship between SN integrity, neural novelty response and subsequent recognition memory performance in individuals with early Alzheimer’s disease.Method79 healthy subjects, 54 participants with subjective cognitive decline (SCD), 18 with mild cognitive impairment (MCI), and 10 individuals with ADD from the DZNE Longitudinal Cognitive Impairment and Dementia (DELCODE) study performed a previously published scene novelty and encoding task (“FADE”) using 3T fMRI. Additional 3D T1‐weighted FLASH images were collected to visualize and quantify the volume and MRI contrast in the SN.Linear regression models were generated to assess for associations between either SN volume and median MRI contrast, considered as proxy integrity measures, with functional activity during novelty and subsequent recognition memory performance (dprime). We used site, gender, age and years of education as covariates. We assessed AD pathology in a subsample of individuals with CSF biomarkers (n = 71).ResultWe observed a significant decline in SN volume (p = 0.003) but not MRI contrast in ADD compared to healthy controls. The SN volume in the whole sample correlated positively with recognition memory for novel stimuli (r = 0.35; p = 0.02,). This association held in the CSF subsample (r = 0.31; p = 0.034) and also after correcting for levels of both tau (phosphotau, r = 0.57; p = 0.0284,) and amyloid (Abeta 42/40 ratio, r = 0.46; p = 0.03,). Additionally, SN median MRI contrast significantly correlated with novelty responses in the hippocampus in the CSF subsample and also in amyloid positive but not negative (Abeta 42/40 ratio<0.08, n = 26) individuals. Finally, recognition memory (p<0.001) was significantly decreased in Abeta positive compared to negative individuals.ConclusionWe show that significant degeneration to the SN in AD dementia and pathological Abeta 42/40 levels are associated with poorer recognition memory for novel stimuli. Moreover, associations between SN median MRI contrast and novelty responses differed between amyloid positive and negative individuals. These results indicate that degeneration to the SN may contribute to memory dysfunction in AD dementia which may be linked to pathological amyloid levels.

Metappearance

There currently exist two main approaches to reproducing visual appearance using Machine Learning (ML): The first is training models that generalize over different instances of a problem, e.g., different images of a dataset. As one-shot approaches, these offer fast inference, but often fall short in quality. The second approach does not train models that generalize across tasks, but rather over-fit a single instance of a problem, e.g., a flash image of a material. These methods offer high quality, but take long to train. We suggest to combine both techniques end-to-end using meta-learning: We over-fit onto a single problem instance in an inner loop, while also learning how to do so efficiently in an outer-loop across many exemplars. To this end, we derive the required formalism that allows applying meta-learning to a wide range of visual appearance reproduction problems: textures, Bidirectional Reflectance Distribution Functions (BRDFs), spatially-varying BRDFs (svBRDFs), illumination or the entire light transport of a scene. The effects of meta-learning parameters on several different aspects of visual appearance are analyzed in our framework, and specific guidance for different tasks is provided. Metappearance enables visual quality that is similar to over-fit approaches in only a fraction of their runtime while keeping the adaptivity of general models.

Personalized synthetic MR imaging with deep learning enhancements.

Personalized synthetic MRI (syn-MRI) uses MR images of an individual subject acquired at a few design parameters (echo time, repetition time, flip angle) to obtain underlying parametric maps, from where MR images of that individual at other design parameter settings are synthesized. However, classical methods that use least-squares (LS) or maximum likelihood estimators (MLE) are unsatisfactory at higher noise levels because the underlying inverse problem is ill-posed. This article provides a pipeline to enhance the synthesis of such images in three-dimensional (3D) using a deep learning (DL) neural network architecture for spatial regularization in a personalized setting where having more than a few training images is impractical. Our DL enhancements employ a Deep Image Prior (DIP) with a U-net type denoising architecture that includes situations with minimal training data, such as personalized syn-MRI. We provide a general workflow for syn-MRI from three or more training images. Our workflow, called DIPsyn-MRI, uses DIP to enhance training images, then obtains parametric images using LS or MLE before synthesizing images at desired design parameter settings. DIPsyn-MRI is implemented in our publicly available Python package DeepSynMRI available at: https://github.com/StatPal/DeepSynMRI. We demonstrate feasibility and improved performance of DIPsyn-MRI on 3D datasets acquired using the Brainweb interface for spin-echo and FLASH imaging sequences, at different noise levels. Our DL enhancements improve syn-MRI in the presence of different intensity nonuniformity levels of the magnetic field, for all but very low noise levels. This article provides recipes and software to realistically facilitate DL-enhanced personalized syn-MRI.

ANGI-05. VASCULAR NORMALIZATION IN MURINE GLIOBLASTOMA IS CONTROLLED BY APLN-SIGNALING AND CAN BE MONITORED BY DCE-MR IMAGING IN VIVO

Abstract OBJECTIVE Glioblastoma (GBM) expansion is accompanied by aberrant tumor vascularization. We demonstrated that the peptide hormone Apelin (APLN) controls GBM neo-vascularization and that the APLN-receptor antagonist Apelin-F13A improved the efficiency and reduced the invasive side effect of established antiangiogenic therapy. Here we investigated if Apelin-F13A blunts the formation of vasogenic edema, which can be monitored by MRI in vivo. METHODS To investigate the role of APLN -signaling in regulating the tightness of the tumor vasculature we performed in vivo leakage assays using Evans-Blue dye and fluorescent dextran. By confocal immunofluorescence, we characterized the maturation of the tumor vasculature with respect to pericyte-coverage and established a dynamic contrast enhanced (DCE) MR imaging protocol to follow vascular edema formation. RESULTS We found that Evans-Blue extravasation is significantly increased by 3-fold in APLNKO tumors compared to controls. Uptake of fluorescent Dextran by CD31+ endothelia was quantified and increased massively from 200μm2 per high magnification field (HMF) in wildtype to 3500μm2 per HMF in APLNKO tumors. Interestingly, intracerebral infusion of Apelin-F13A enhanced pericyte coverage of the tumor vasculature by 50%, decreased Evans-Blue extravasation from 25 μg/ml in controls to 8 μg/ml in treated tumors significantly and efficiently reversed the APLN-dependent vasogenic edema assessed by comparison of T2w-MRI to HE tumor volumes. To follow vasogenic edema formation in vivo, T1w 3D FLASH images were acquired every second over a 360s time course after gadolinium-based MR-contrast agent injection and demonstrated a delayed washout of the contrast in APLN-deficient GBM. CONCLUSION Together, our study shows that DCE-MRI can document APLN-dependent intratumoral vascular normalization and allows inspecting vasogenic edema formation in vivo. In addition to its anti-angiogenic / anti-invasive effect, Apelin-F13A can potently reduce vasogenic edema and might thus serve as a multimodal therapy for the treatment of human GBM in the future.

Measurement of the Attenuation Coefficient in Fresh Water Using the Adjacent Frame Difference Method

The attenuation coefficient of a water body is one of the important factors for describing its features. However, its remote measurement in real time is still a challenge. In this paper, we demonstrated a novel method to realize real-time remote measurements of the attenuation coefficient of fresh water using flash imaging lidar based on the adjacent frame difference (AFD) method and a water body backscattering model. In general, we firstly investigated the relationship between the backscattering intensity and the attenuation coefficient based on the backscattering model of the water body. Then, the backscattering intensity at the front and back edges of the range-gate obtained by the AFD method was brought into this relationship to obtain the attenuation coefficient. Experiments on the measurements of the average attenuation coefficient of the 532 nm laser in fresh water at 3–8 m were further carried out using our self-developed flash lidar with the AFD method. The acquired water attenuation coefficients were 0.1334±0.02 and 0.1382±0.03 with a delay step time of 1 ns and 2 ns in the AFD method, respectively. We compared these values to the one achieved following the conventional Beer–Lambert law (0.1330±0.02), and they matched well with each other. These results fully illustrated the feasibility and reliability of the proposed method for measuring the attenuation coefficient of water bodies.

DiffeoRaptor: diffeomorphic inter-modal image registration using RaPTOR.

Diffeomorphic image registration is essential in many medical imaging applications. Several registration algorithms of such type have been proposed, but primarily for intra-contrast alignment. Currently, efficient inter-modal/contrast diffeomorphic registration, which is vital in numerous applications, remains a challenging task. We proposed a novel inter-modal/contrast registration algorithm that leverages Robust PaTch-based cOrrelation Ratio metric to allow inter-modal/contrast image alignment and bandlimited geodesic shooting demonstrated in Fourier-Approximated Lie Algebras (FLASH) algorithm for fast diffeomorphic registration. The proposed algorithm, named DiffeoRaptor, was validated with three public databases for the tasks of brain and abdominal image registration while comparing the results against three state-of-the-art techniques, including FLASH, NiftyReg, and Symmetric image Normalization (SyN). Our results demonstrated that DiffeoRaptor offered comparable or better registration performance in terms of registration accuracy. Moreover, DiffeoRaptor produces smoother deformations than SyN in inter-modal and contrast registration. The code for DiffeoRaptor is publicly available at https://github.com/nimamasoumi/DiffeoRaptor .

Adjacent Frame Difference with Dynamic Threshold Method in Underwater Flash Imaging LiDAR

During the underwater LiDAR imaging process, the images achieved by the conventional constant threshold adjacent frame difference (AFD) method normally loses the distance information of targets. This is mainly due to the Gaussian distribution of the laser light intensity field, which leads to the inhomogeneous intensity distribution in the frame from the target acquired by intensity charge-coupled devices (ICCD). In order to overcome this issue, the novel dynamic threshold adjacent frame difference (DTAFD) method was proposed in this paper. The DTAFD method modifies the intensity threshold following the pixel intensities in the different parts of the single frame intensity image acquired by ICCD. After the detailed theoretical demonstration of the DTAFD method, with the purpose of verifying its feasibility, the self-developed range-gated flash imaging LiDAR has been employed to construct the distance images of the rectangular and circular shaped targets at different distances. The distance between the rectangular target and the LiDAR system is 25.7 m, and the circular target is 70 cm further away from the rectangular target. The full distance information of these two targets is obtained by the DTAFD method with an effectively suppressing noise and the PSNR is increased from 6.95±0.0426 dB to 7.62±0.0264 dB. The experimental results indicate that the DTAFD method efficiently solves the AFD method’s drawback on the target information loss caused by the unequal optical field distribution, which makes it more suitable for the scenarios with uneven laser distribution such as the underwater imaging environment.

A Delay Modulation Method to Improve the Time Resolution for a 32 × 32 Array of MPPC Image Sensor

The array laser detector can achieve three-dimensional (3D) flash imaging via multi-channel parallel ranging, which significantly improves the imaging speed. However, the large number of the array makes it difficult to improve the ranging resolution through circuit evolution and data processing, even the imaging speed will be sacrificed. To overcome the problem, a delay modulation method on the transmitter of the laser is proposed in this study. The method is realized by using a 32×32 Multi-Pixel Photon Counter (MPPC) array and an improved phase-shifted clocks method. We eventually achieve 1024-channel 3D imaging that exceeds the depth resolution of the system. Experimental results show that, the Root Mean Square Error (RMSE) of plane fitting with delay modulation is 2.39-5.03 cm, which has come down to 48.6% than those without delay modulation. Using the delay modulation method, a target with a depth difference of about 4 cm can be clearly distinguished by 3D imaging at a distance of 5 m, even if the minimum resolution of the system is 5 cm. Results also demonstrate that the proposed method can easily and effectively enhance the measurement accuracy of 3D flash imaging.

Visual turn as an interpretive model of stage synthesis of arts in the culture of the XX–XXI century

Visual turn as an interpretive model of stage synthesis of arts in the culture of the XX–XXI century Purpose of the Article. The research is related to the definition of visual and virtual dimensions of the interpretation of fashion activity as a certain communicative scene. The research methodology is to apply a retrospective analysis of visual patterns in the stage space of everyday culture. The scientific novelty of the work is that the scene is perceived by the recipient as an optical phenomenon, has a wide space in which the actor of action or event performs certain communicative acts verbally and optically. The components of this synthesis plastically, gesturally, verbally-dramatically and optically-visually present a whole selection of arts that form a stage synthesis. Drama is verbalized as a plastic behavior, where the action, movements determine the high space of mise-en-scène of the tank, which testifies to both the expressive arts known since ancient Greek chorea and the visual-optical patterns. Thus, in the context of visual rotation, ocular centrism is actualized - trust in the picture. Conclusions. It should be noted that there is an artistic critique of the media, producers, galleries, magazines, that is, a space of unification and adequate representation of various art phenomena is formed. It is not the artists themselves, the actors of the big stage, the producers of the great culture of everyday life who live a full life, but those who create art events, local scenes of the presentation. Visual research tends to eclecticism, which turns into a polymorphic set of discourses, which indicates that you need to find a comfort zone, certain attractions of everyday culture. Without them, the realization of a modern art product is no longer possible. Advertising management, imageology are presented in society as an indirect reality, similar to virtual technology. Imagination as a video presentation becomes a problem of interpretation, a problem of vision. Visual culture tries to rehabilitate the image and determine its meaning without a name, without a name. Such a culture without a name leads to the fact that researchers are beginning to determine only what has a name. Art history describes the relationships between objects, chronological order, movement, formal preferences, and iconographic data. Needless to say, the work of art in this case remains an individual creation, a special source of life, and a social text. The culture of everyday life in the context of visual rotation is simultaneously a text, an image, a stage artifact, a pattern, and a flash image. Key words: culture, culture of everyday life, stage synthesis of culture of everyday life, image, visual turn.

Future Developments in Charged Particle Therapy: Improving Beam Delivery for Efficiency and Efficacy.

The physical and clinical benefits of charged particle therapy (CPT) are well recognized. However, the availability of CPT and complete exploitation of dosimetric advantages are still limited by high facility costs and technological challenges. There are extensive ongoing efforts to improve upon these, which will lead to greater accessibility, superior delivery, and therefore better treatment outcomes. Yet, the issue of cost remains a primary hurdle as utility of CPT is largely driven by the affordability, complexity and performance of current technology. Modern delivery techniques are necessary but limited by extended treatment times. Several of these aspects can be addressed by developments in the beam delivery system (BDS) which determines the overall shaping and timing capabilities enabling high quality treatments. The energy layer switching time (ELST) is a limiting constraint of the BDS and a determinant of the beam delivery time (BDT), along with the accelerator and other factors. This review evaluates the delivery process in detail, presenting the limitations and developments for the BDS and related accelerator technology, toward decreasing the BDT. As extended BDT impacts motion and has dosimetric implications for treatment, we discuss avenues to minimize the ELST and overview the clinical benefits and feasibility of a large energy acceptance BDS. These developments support the possibility of advanced modalities and faster delivery for a greater range of treatment indications which could also further reduce costs. Further work to realize methodologies such as volumetric rescanning, FLASH, arc, multi-ion and online image guided therapies are discussed. In this review we examine how increased treatment efficiency and efficacy could be achieved with improvements in beam delivery and how this could lead to faster and higher quality treatments for the future of CPT.

Generative modelling of BRDF textures from flash images

We learn a latent space for easy capture, consistent interpolation, and efficient reproduction of visual material appearance. When users provide a photo of a stationary natural material captured under flashlight illumination, first it is converted into a latent material code. Then, in the second step, conditioned on the material code, our method produces an infinite and diverse spatial field of BRDF model parameters (diffuse albedo, normals, roughness, specular albedo) that subsequently allows rendering in complex scenes and illuminations, matching the appearance of the input photograph. Technically, we jointly embed all flash images into a latent space using a convolutional encoder, and -conditioned on these latent codes- convert random spatial fields into fields of BRDF parameters using a convolutional neural network (CNN). We condition these BRDF parameters to match the visual characteristics (statistics and spectra of visual features) of the input under matching light. A user study compares our approach favorably to previous work, even those with access to BRDF supervision. Project webpage: https://henzler.github.io/publication/neuralmaterial/.

Pump-probe X-ray holographic imaging of laser-induced cavitation bubbles with femtosecond FEL pulses

Cavitation bubbles can be seeded from a plasma following optical breakdown, by focusing an intense laser in water. The fast dynamics are associated with extreme states of gas and liquid, especially in the nascent state. This offers a unique setting to probe water and water vapor far-from equilibrium. However, current optical techniques cannot quantify these early states due to contrast and resolution limitations. X-ray holography with single X-ray free-electron laser pulses has now enabled a quasi-instantaneous high resolution structural probe with contrast proportional to the electron density of the object. In this work, we demonstrate cone-beam holographic flash imaging of laser-induced cavitation bubbles in water with nanofocused X-ray free-electron laser pulses. We quantify the spatial and temporal pressure distribution of the shockwave surrounding the expanding cavitation bubble at time delays shortly after seeding and compare the results to numerical simulations.

A Guided Edge-Aware Smoothing-Sharpening Filter Based on Patch Interpolation Model and Generalized Gamma Distribution

Smoothing and sharpening are two fundamental image processing operations. The latter is usually related to the former through the unsharp masking algorithm. In this paper, we develop a new type of filter which performs smoothing or sharpening via a tuning parameter. The development of the new filter is based on (1) a new Laplacian-based filter formulation which unifies the smoothing and sharpening operations, (2) a patch interpolation model similar to that used in the guided filter which provides edge-awareness capability, and (3) the generalized Gamma distribution which is used as the prior for parameter estimation. We have conducted detailed studies on the properties of two versions of the proposed filter (self-guidance and external guidance). We have also conducted experiments to demonstrate applications of the proposed filter. In the self-guidance case, we have developed adaptive smoothing and sharpening algorithms based on texture, depth and blurriness information extracted from an image. Applications include enhancing human face images, producing shallow depth of field effects, focus-based image enhancement, and seam carving. In the external guidance case, we have developed new algorithms for combining flash and no-flash images and for enhancing multi-spectral images using a panchromatic image.