High Dynamic Range Research Articles

Probing PAC1 receptor activation across species with an engineered sensor.

Class-B1 G-protein-coupled receptors (GPCRs) are an important family of clinically relevant drug targets that remain difficult to investigate via high-throughput screening and in animal models. Here, we engineered PAClight1P78A, a novel genetically encoded sensor based on a class-B1 GPCR (the human PAC1 receptor, hmPAC1R) endowed with high dynamic range (ΔF/F0 = 1100%), excellent ligand selectivity, and rapid activation kinetics (τON = 1.15 s). To showcase the utility of this tool for in vitro applications, we thoroughly characterized and compared its expression, brightness and performance between PAClight1P78A-transfected and stably expressing cells. Demonstrating its use in animal models, we show robust expression and fluorescence responses upon exogenous ligand application ex vivo and in vivo in mice, as well as in living zebrafish larvae. Thus, the new GPCR-based sensor can be used for a wide range of applications across the life sciences empowering both basic research and drug development efforts.

HDR-ChipQA: No-reference quality assessment on High Dynamic Range videos

We present a no-reference video quality model and algorithm that delivers standout performance for High Dynamic Range (HDR) videos, which we call HDR-ChipQA. HDR videos represent wider ranges of luminances, details, and colors than Standard Dynamic Range (SDR) videos. The growing adoption of HDR in massively scaled video networks has driven the need for video quality assessment (VQA) algorithms that better account for distortions on HDR content. In particular, standard VQA models may fail to capture conspicuous distortions at the extreme ends of the dynamic range, because the features that drive them may be dominated by distortions that pervade the mid-ranges of the signal. We introduce a new approach whereby a local expansive nonlinearity emphasizes distortions occurring at the higher and lower ends of the local luma range, allowing for the definition of additional quality-aware features that are computed along a separate path. These features are not HDR-specific, and also improve VQA on SDR video contents, albeit to a reduced degree. We show that this preprocessing step significantly boosts the power of distortion-sensitive natural video statistics (NVS) features when used to predict the quality of HDR content. In similar manner, we separately compute novel wide-gamut color features using the same nonlinear processing steps. We have found that our model significantly outperforms SDR VQA algorithms on the only publicly available, comprehensive HDR database, while also attaining state-of-the-art performance on SDR content.

Characterisation of the Glare Properties of Photovoltaic Modules and PV Systems Using a Photographic Camera

With the increasing prevalence of photovoltaic (PV) installations on north-facing roofs in urban areas, the issue of glare on PV modules is becoming more significant. This paper presents a method for measuring and characterising the intensity and type of glare using a commercially available photo camera. The method utilises the fact that brightness information is inherently contained in a photograph. Due to the strong non-linearity of brightness information in an image, especially when using High Dynamic Range (HDR) functionality, an image needs to be divided into multiple sub-images with clearly defined settings, and the linear range extracted from each of them. The information obtained in this way is then assembled into a new image. The resulting luminance image describes the intensity and shape of glare, forming the basis for calculating the occurrence of glare (times and days of the year) in a simplified manner.

A High Dynamic Range Ion Detector for Multireflection Time-of-Flight Analyzers.

Reflectron-based time-of-flight analyzers rely on subnanosecond detector time response to achieve acceptable resolving power for low-mid-mass, multiple-ion peaks. With the adoption of multireflection analyzers, order of magnitude longer folded ion paths relax restrictions on detector response time, allowing implementation of new technologies that greatly improve dynamic range, detector lifetime, and ion detection efficiency. A detection system is presented, integrated into a multireflection analyzer, that combines 10 keV postacceleration and focal plane correction with a unique BxE focusing, optically coupled detector, preamplification, and dual-channel digitization. Calibration and peak-handling methods are also described. The instrument demonstrated >1 × 104 dynamic range in a single shot, > 100k resolving power, and a relative immunity to detector aging.

A Hybrid Neuromorphic Object Tracking and Classification Framework for Real-Time Systems.

Deep learning inference that needs to largely take place on the "edge" is a highly computational and memory intensive workload, making it intractable for low-power, embedded platforms such as mobile nodes and remote security applications. To address this challenge, this article proposes a real-time, hybrid neuromorphic framework for object tracking and classification using event-based cameras that possess desirable properties such as low-power consumption (5-14 mW) and high dynamic range (120 dB). Nonetheless, unlike traditional approaches of using event-by-event processing, this work uses a mixed frame and event approach to get energy savings with high performance. Using a frame-based region proposal method based on the density of foreground events, a hardware-friendly object tracking scheme is implemented using the apparent object velocity while tackling occlusion scenarios. The frame-based object track input is converted back to spikes for TrueNorth (TN) classification via the energy-efficient deep network (EEDN) pipeline. Using originally collected datasets, we train the TN model on the hardware track outputs, instead of using ground truth object locations as commonly done, and demonstrate the ability of our system to handle practical surveillance scenarios. As an alternative tracker paradigm, we also propose a continuous-time tracker with C++ implementation where each event is processed individually, which better exploits the low latency and asynchronous nature of neuromorphic vision sensors. Subsequently, we extensively compare the proposed methodologies to state-of-the-art event-based and frame-based methods for object tracking and classification, and demonstrate the use case of our neuromorphic approach for real-time and embedded applications without sacrificing performance. Finally, we also showcase the efficacy of the proposed neuromorphic system to a standard RGB camera setup when simultaneously evaluated over several hours of traffic recordings.

Silicon-Based Piezoresistive Stress Sensor Arrays for Use in Flexible Tactile Skin.

Bioinspired robotics and smart prostheses have many applications in the healthcare sector. Patients can use them for rehabilitation or day-to-day assistance, allowing them to regain some agency over their movements. The most common way to make these smart artificial limbs is by adding a "human-like" electronic skin to detect force and emulate touch detection. This paper presents a fully integrated CMOS-based stress sensor design with a high dynamic range (100 kPa to 100 MPa) supported by an adaptive gain-controlled chopping amplifier. The sensor chip includes four identical sensing structures capable of measuring the chip's local stress gradient and complete readout circuitry supporting data transfer via I2C protocol. The sensor takes 10.2 ms to measure through all four structures and goes into a low-power mode when not in use. The designed chip consumes a total current of ∼300 μA for one complete operation cycle and ∼30 μA during low power mode in simulations. Moreover, the complete design is CMOS-based, making it easier for large-scale commercial fabrication and more affordable for patients in the long run. This paper further proposes the concept of a tactile smart skin by integrating a network of sensor chips with flexible polymers.

SGformer: Boosting transformers for indoor lighting estimation from a single image

Estimating lighting from standard images can effectively circumvent the need for resource-intensive high-dynamic-range (HDR) lighting acquisition. However, this task is often ill-posed and challenging, particularly for indoor scenes, due to the intricacy and ambiguity inherent in various indoor illumination sources. We propose an innovative transformer-based method called SGformer for lighting estimation through modeling spherical Gaussian (SG) distributions—a compact yet expressive lighting representation. Diverging from previous approaches, we explore underlying local and global dependencies in lighting features, which are crucial for reliable lighting estimation. Additionally, we investigate the structural relationships spanning various resolutions of SG distributions, ranging from sparse to dense, aiming to enhance structural consistency and curtail potential stochastic noise stemming from independent SG component regressions. By harnessing the synergy of local-global lighting representation learning and incorporating consistency constraints from various SG resolutions, the proposed method yields more accurate lighting estimates, allowing for more realistic lighting effects in object relighting and composition. Our code and model implementing our work can be found at https://github.com/junhong-jennifer-zhao/SGformer.

Three phase spectral interferometry for recording high dynamic range optical waveforms with <1 ps resolution over >2 ns records applied to closed-loop pulse shaping

We demonstrate, to our knowledge, a novel spectral interferometry technique that simultaneously captures three spectral interferograms of a signal waveform with a reference pulse. The measured performance is robust to nonidealities and ambient drifts by implementing a precisely calibrated 3×3 polarization maintaining (PM) splitter that provides three phase shift differences nominally spaced 120° apart. The system can achieve long record length by implementing three, high resolution, virtually imaged phase array (VIPA) spectrometers. Here, we experimentally implement this technique and demonstrate the measurement of waveforms with >2ns of record while maintaining <1ps resolution.

Dosimetry of microbeam radiotherapy by flexible hydrogenated amorphous silicon detectors

Objective. Detectors that can provide accurate dosimetry for microbeam radiation therapy (MRT) must possess intrinsic radiation hardness, a high dynamic range, and a micron-scale spatial resolution. In this work we characterize hydrogenated amorphous silicon detectors for MRT dosimetry, presenting a novel combination of flexible, ultra-thin and radiation-hard features. Approach. Two detectors are explored: an n-type/intrinsic/p-type planar diode (NIP) and an NIP with an additional charge selective layer (NIP + CSC). Results. The sensitivity of the NIP + CSC detector was greater than the NIP detector for all measurement conditions. At 1 V and 0 kGy under the 3T Cu–Cu synchrotron broadbeam, the NIP + CSC detector sensitivity of (7.76 ± 0.01) pC cGy−1 outperformed the NIP detector sensitivity of (3.55 ± 0.23) pC cGy−1 by 219%. The energy dependence of both detectors matches closely to the attenuation coefficient ratio of silicon against water. Radiation damage measurements of both detectors out to 40 kGy revealed a higher radiation tolerance in the NIP detector compared to the NIP + CSC (17.2% and 33.5% degradations, respectively). Percentage depth dose profiles matched the PTW microDiamond detector’s performance to within ±6% for all beam filtrations except in 3T Al–Al due to energy dependence. The 3T Cu–Cu microbeam field profile was reconstructed and returned microbeam width and peak-to-peak values of (51 ± 1) μm and (405 ± 5) μm, respectively. The peak-to-valley dose ratio was measured as a function of depth and agrees within error to the values obtained with the PTW microDiamond. X-ray beam induced charge mapping of the detector revealed minimal dose perturbations from extra-cameral materials. Significance. The detectors are comparable to commercially available dosimeters for quality assurance in MRT. With added benefits of being micron-sized and possessing a flexible water-equivalent substrate, these detectors are attractive candidates for quality assurance, in-vivo dosimetry and in-line beam monitoring for MRT and FLASH therapy.

Robust polarimetric dehazing algorithm based on low-rank approximation and multiple virtual-exposure fusion

Polarimetric dehazing is an effective way to enhance the quality of images captured in foggy weather. However, images of essential polarization parameters are vulnerable to noise, and the brightness of dehazed images is usually unstable due to different environmental illuminations. These two weaknesses reveal that current polarimetric dehazing algorithms are not robust enough to deal with different scenarios. This paper proposes a novel, to our knowledge, and robust polarimetric dehazing algorithm to enhance the quality of hazy images, where a low-rank approximation method is used to obtain low-noise polarization parameter images. Besides, in order to improve the brightness stability of the dehazed image and thus keep the image have more details within the standard dynamic range, this study proposes a multiple virtual-exposure fusion (MVEF) scheme to process the dehazed image (usually having a high dynamic range) obtained through polarimetric dehazing. Comparative experiments show that the proposed dehazing algorithm is robust and effective, which can significantly improve overall quality of hazy images captured under different environments.

Towards a Flexible Semantic Guided Model for Single Image Enhancement and Restoration.

Low-light image enhancement (LLIE) investigates how to improve the brightness of an image captured in illumination-insufficient environments. The majority of existing methods enhance low-light images in a global and uniform manner, without taking into account the semantic information of different regions. Consequently, a network may easily deviate from the original color of local regions. To address this issue, we propose a semantic-aware knowledge-guided framework (SKF) that can assist a low-light enhancement model in learning rich and diverse priors encapsulated in a semantic segmentation model. We concentrate on incorporating semantic knowledge from three key aspects: a semantic-aware embedding module that adaptively integrates semantic priors in feature representation space, a semantic-guided color histogram loss that preserves color consistency of various instances, and a semantic-guided adversarial loss that produces more natural textures by semantic priors. Our SKF is appealing in acting as a general framework in the LLIE task. We further present a refined framework SKF++ with two new techniques: (a) Extra convolutional branch for intra-class illumination and color recovery through extracting local information and (b) Equalization-based histogram transformation for contrast enhancement and high dynamic range adjustment. Extensive experiments on various benchmarks of LLIE task and other image processing tasks show that models equipped with the SKF/SKF++ significantly outperform the baselines and our SKF/SKF++ generalizes to different models and scenes well. Besides, the potential benefits of our method in face detection and semantic segmentation in low-light conditions are discussed. The code and pre-trained models have been publicly available at https://github.com/langmanbusi/Semantic-Aware-Low-Light-Image-Enhancement.

Momentum-Space Observation of Optically Excited Nonthermal Electrons in Graphene with Persistent Pseudospin Polarization.

The unique optical properties of graphene, with broadband absorption and ultrafast response, make it a critical component of optoelectronic and spintronic devices. Using time-resolved momentum microscopy with high data rate and high dynamic range, we report momentum-space measurements of electrons promoted to the graphene conduction band with visible light and their subsequent relaxation. We observe a pronounced nonthermal distribution of nascent photoexcited electrons with lattice pseudospin polarization in remarkable agreement with results of simple tight-binding theory. By varying the excitation fluence, we vary the relative importance of electron-electron vs electron-phonon scattering in the relaxation of the initial distribution. Increasing the excitation fluence results in increased noncollinear electron-electron scattering and reduced pseudospin polarization, although up-scattered electrons retain a degree of polarization. These detailed momentum-resolved electron dynamics in graphene demonstrate the capabilities of high-performance time-resolved momentum microscopy in the study of 2D materials and can inform the design of graphene devices.

Uncertainty estimation in HDR imaging with Bayesian neural networks

The goal of high dynamic range (HDR) imaging is to estimate potential high-quality images from multi-exposed low dynamic range (LDR) inputs. Intuitively, there exist various possible HDR images corresponding to given LDR inputs, which results in uncertainty in the estimated results. However, most existing HDR imaging methods employ l1 or l2 loss only to provide one possible estimation from various possible solutions, which fails to model the uncertainty, and thus lacks high-frequency details. In this work, we design Bayesian neural networks to capture the uncertainty, which can model one-to-many relations and provide various possible solutions. Concretely, we propose a Variational Bayesian Layer by leveraging a hierarchical prior on the network weights and inferring a new joint posterior, which is utilized to model uncertainty in high-frequency details (e.g., textures), and model uncertainty in semantic information (e.g., ghost areas), respectively. By leveraging Bayesian framework, the proposed method can provide various potential high-quality estimations, especially in high-frequency details. Experiments on different datasets show that the proposed method enables sampling possible HDR imaging, and the consensus estimate achieves state-of-the-art quantitative and qualitative results.

Bayesian multi-exposure image fusion for robust high dynamic range ptychography

The limited dynamic range of the detector can impede coherent diffractive imaging (CDI) schemes from achieving diffraction-limited resolution. To overcome this limitation, a straightforward approach is to utilize high dynamic range (HDR) imaging through multi-exposure image fusion (MEF). This method involves capturing measurements at different exposure times, spanning from under to overexposure and fusing them into a single HDR image. The conventional MEF technique in ptychography typically involves subtracting the background noise, ignoring the saturated pixels and then merging the acquisitions. However, this approach is inadequate under conditions of low signal-to-noise ratio (SNR). Additionally, variations in illumination intensity significantly affect the phase retrieval process. To address these issues, we propose a Bayesian MEF modeling approach based on a modified Poisson distribution that takes the background and saturation into account. The expectation-maximization (EM) algorithm is employed to infer the model parameters. As demonstrated with synthetic and experimental data, our approach outperforms the conventional MEF method, offering superior phase retrieval under challenging experimental conditions. This work underscores the significance of robust multi-exposure image fusion for ptychography, particularly in imaging shot-noise-dominated weakly scattering specimens or in cases where access to HDR detectors with high SNR is limited. Furthermore, the applicability of the Bayesian MEF approach extends beyond CDI to any imaging scheme that requires HDR treatment. Given this versatility, we provide the implementation of our algorithm as a Python package.

Generic color correction for tone mapping operators in high dynamic range imaging

One of the significant challenges in tone mapping is to preserve the perceptual quality of the tone-mapped images. Traditional tone mapping operators (TMOs) compress the luminance of high dynamic range (HDR) images with little to no consideration of image color information resulting into suboptimal colors. Existing color management algorithms require either manual parameter tuning or introducing lightness and hue shifts. The current study aimed to develop a color correction model in plausible agreement with the psychophysical phenomenon of color appearance perception for optimal color reproduction of tone mapped images. The proposed model leverages CIECAM16 perceptual correlates i.e., lightness, chroma, and hue. It is adaptive and entirely automatic while preserving the lightness of the tone-mapped image and maintaining the hue from the HDR image. Moreover, it does not require any gamut mapping correction algorithm in natural color reproduction. The effectiveness of the model was validated using objective and subjective methods. The experiments revealed that the model achieved optimal color reproduction in terms of the naturalness of the colors.

Water penetrating radar: A fluvial scour study of antenna performance and data processing

AbstractGround‐penetrating radar (syn. water‐penetrating radar, or WPR hereon) has been used previously in the detection and characterisation of riverine scour. This paper presents field data, equipment calibration and processing output results from a new generation of high dynamic range (HDR) radar systems. These provide a new and more sophisticated model of fluvial scour for the study site. The adaptability for deployment on water, wide bandwidth and good quality in raw and processed data, demonstrates the advantages of the system. Data processing and depth calibration are issues in the interpretation of WPR data, which are both discussed. WPR demonstrates the potential for surveying sedimentation where palaeoscour occurred and thus anticipated. This maybe in conjunction with other techniques or where aquatic vegetation, rocky substrates or methane gas bubble release preclude use of sonar.

Immersive representation for visualizing surface temperature and integrative lighting in architecture

This paper illustrates the potential of representing and reading environmental data captured for immersive visualization of indoor built environments. This study introduces methods for qualitative representation of light and heat in aligned viewing directions in immersive scenes. It employs a novel technique for capturing High Dynamic Range (HDR) and thermal images in 360-degree directions. This technique allows visualization of radiant temperature and integrative lighting variations associated with the viewing direction and time. The highlight of these visualizations is the identical viewing points in studying the indoor ambiances. The paper provides visualizations derived from hourly data captures in architectural studios through a 360-degree thermal-visual capturing device. The paper aims to present these results through various visualization formats, including architectural planar presentations, diagrams, and colour maps, showing surface temperature and Photopic and Melanopic illuminance intensities. Architectural insights gained from each of these visualizations are highlighted and discussed. Specifically, the results of this research illustrate the potential of the immersive image-based environmental assessment and visualization method for representing indoor ambiances regarding their non-visible thermal and lighting conditions through time and across all viewing directions within the space.

Rapid-scan broadband frequency-domain terahertz spectroscopy via dynamic optical phase lock

Frequency-domain terahertz (THz) spectroscopy using photomixing devices has unique advantages such as high dynamic range and high spectral resolution. Thus, many applications for solid-state and gas-phase spectroscopy have been proposed. In this study, we developed a feedback controlled technique to dynamically compensate for the optical phase accompanied by frequency sweep, enabling both fast and high-resolution data acquisition across a wide frequency region. From gas-phase THz spectroscopy measurements of dilute acetonitrile gas in a wide frequency range up to 1.1 THz, fine structures with linewidths less than 10 MHz were clearly resolved, while the data acquisition rate was improved by two orders compared to the previously reported value.

Color Volumetric Imaging With High Dynamic Range Based on Parallel Confocal Detection

Color Volumetric Imaging With High Dynamic Range Based on Parallel Confocal Detection

A 4–8 GHz kinetic inductance traveling-wave parametric amplifier using four-wave mixing with near quantum-limited noise performance

Kinetic inductance traveling-wave parametric amplifiers (KI-TWPAs) have a wide instantaneous bandwidth with a near quantum-limited noise performance and a relatively high dynamic range. Because of this, they are suitable readout devices for cryogenic detectors and superconducting qubits and have a variety of applications in quantum sensing. This work discusses the design, fabrication, and performance of a KI-TWPA based on four-wave mixing in a NbTiN microstrip transmission line. This device amplifies a signal band from 4 to 8 GHz without contamination from image tones, which are produced in a separate higher frequency band. The 4–8 GHz band is commonly used to read out cryogenic detectors, such as microwave kinetic inductance detectors and Josephson junction-based qubits. We report a measured maximum gain of over 20 dB using four-wave mixing with a 1 dB gain compression point of −58 dBm at 15 dB of gain over that band. The bandwidth and peak gain are tunable by adjusting the pump-tone frequency and power. Using a Y-factor method, we measure an amplifier-added noise of 0.5 ≤ Nadded ≤ 1.5 photons from 4.5 to 8 GHz.