Dynamic Range Images Research Articles

Improved fast deconvolution algorithms based on functional beamforming for gas leakage sound source imaging

Fast deconvolution algorithms have high computational efficiency, but they often overlook spatial variations in the point spread function (PSF) and wrap-around errors, resulting in the degradation of sound source imaging performance. Accordingly, this paper proposes two improved fast deconvolution algorithms based on functional beamforming (FB). The proposed algorithms establish a linear equation involving the FB output, sound source distribution, and raised-power spatial shift-invariant PSF, specially with irregular focus grid point. Furthermore, iterative solutions are obtained based on fast Fourier transform, enhancing imaging performance while maintaining computational efficiency. Simulations and experimental results demonstrate that compared to the fast deconvolution algorithms, the proposed algorithms expand the effective imaging area to 36.3°, and the Rényi entropy is reduced by approximately 45 %, which has higher imaging spatial resolution and dynamic range. Finally, the compressed gas leakage source imaging results validate the effectiveness of the proposed algorithms, indicating that they have promising engineering application prospects.

Non-iterative model-based inversion for low channel-count optical ultrasound imaginga).

Ultrasound image reconstruction is typically performed using the computationally efficient delay-and-sum algorithm. However, this algorithm is suboptimal for systems of low channel counts, where it causes significant image artefacts. These artefacts can be suppressed through model-based inversion approaches; however, their computational costs typically prohibit real-time implementations. In this work, the emerging optical ultrasound (OpUS) modality is considered, where ultrasound waves are both generated and detected using light. With this modality, imaging probes comprise very low channel counts, resulting in significant image artefacts that limit the imaging dynamic range. However, this low channel counts offer an opportunity for non-iterative ("direct") model-based inversion (DMI) on modest computational resources available in a typical workstation. When applied to both synthetic and experimental OpUS data, the presented DMI method achieved substantial reduction in image artefacts and noise, improved recovery of image amplitudes, and-after one-off pre-computation of the system matrices-significantly reduced reconstruction time, even in imaging scenarios exhibiting mild spatial inhomogeneity. Whilst here applied to an OpUS imaging system, DMI can be applied to other low channel-count imaging systems, and is therefore expected to achieve better image quality, reduce system complexity, or both, in a wide range of settings.

A Bio-Inspired Retinal Model as a Prefiltering Step Applied to Letter and Number Recognition on Chilean Vehicle License Plates

This paper presents a novel use of a bio-inspired retina model as a scene preprocessing stage for the recognition of letters and numbers on Chilean vehicle license plates. The goal is to improve the effectiveness and ease of pattern recognition. Inspired by the responses of mammalian retinas, this retinal model reproduces both the natural adjustment of contrast and the enhancement of object contours by parvocellular cells. Among other contributions, this paper provides an in-depth exploration of the architecture, advantages, and limitations of the model; investigates the tuning parameters of the model; and evaluates its performance when integrating a convolutional neural network and a spiking neural network into an optical character recognition (OCR) algorithm, using 40 different genuine license plate images as a case study and for testing. The results obtained demonstrate the reduction of error rates in character recognition based on convolutional neural networks (CNNs), spiking neural networks (SNNs), and OCR. It is concluded that this bio-inspired retina model offers a wide spectrum of potential applications to further explore, including motion detection, pattern recognition, and improvement of dynamic range in images, among others.

Hyperspectral image dynamic range reconstruction using deep neural network-based denoising methods

Hyperspectral (HS) measurement is among the most useful tools in agriculture for early disease detection. However, the cost of HS cameras that can perform the desired detection tasks is prohibitive-typically fifty thousand to hundreds of thousands of dollars. In a previous study at the Agricultural Research Organization’s Volcani Institute (Israel), a low-cost, high-performing HS system was developed which included a point spectrometer and optical components. Its main disadvantage was long shooting time for each image. Shooting time strongly depends on the predetermined integration time of the point spectrometer. While essential for performing monitoring tasks in a reasonable time, shortening integration time from a typical value in the range of 200 ms to the 10 ms range results in deterioration of the dynamic range of the captured scene. In this work, we suggest correcting this by learning the transformation from data measured with short integration time to that measured with long integration time. Reduction of the dynamic range and consequent low SNR were successfully overcome using three developed deep neural networks models based on a denoising auto-encoder, DnCNN and LambdaNetworks architectures as a backbone. The best model was based on DnCNN using a combined loss function of ℓ2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\ell _{2}$$\\end{document} and Kullback–Leibler divergence on images with 20 consecutive channels. The full spectrum of the model achieved a mean PSNR of 30.61 and mean SSIM of 0.9, showing total improvement relatively to the 10 ms measurements’ mean PSNR and mean SSIM values by 60.43% and 94.51%, respectively.

Infrared Image Dynamic Range Compression based on Adaptive Contrast Adjustment and Structure Preservation

Infrared Image Dynamic Range Compression based on Adaptive Contrast Adjustment and Structure Preservation

Laser speckle contrast imaging based on spatial frequency domain filtering.

We proposed a novel method to separate static and dynamic speckles based on spatial frequency domain filtering. First, the raw speckle image sequence is processed frame by frame through 2D Fourier transform, low-pass and high-pass filtering in the spatial frequency domain, and inverse Fourier transform. Then, we can obtain low- and high-frequency image sequences in the spatial domain. Second, we averaged both sequences in the time domain. After the above processing, we obtain the mean intensities of the dynamic and static speckle components in the spatial domain. Finally, we calculated the time-averaged modulation depth to map the 2-D blood flow distribution. Both phantom and vivo experiments demonstrated that the proposed method could effectively suppress the background non-uniformity and has the advantage of high computational efficiency. It also can effectively improve image contrast, contrast-to-noise ratio, and imaging dynamic range.

A new method for high dynamic range 3D measurement combining adaptive fringe projection and original-inverse fringe projection

For fringe projection profilometry (FPP), high reflections on metal, plastic, and ceramic surfaces cause intensity saturation, resulting in three dimensional (3D) measurement errors. The measurement efficiency using the multiple exposure method (MEM) is low and the exposure time is difficult to quantify. The original and inverse fringe projection method (OIFPM) suffers from saturation of the opposite fringes on highly reflective surfaces. The adaptive fringe projection method (AFPM) is still limited by image dynamic range and pixel mapping accuracy. To this end, this paper proposes a new scheme combining OIFPM and AFPM, called the original and inverse adaptive fringe projection method (OIAFPM). Firstly, an adaptive intensity template in the camera coordinate system is generated. Secondly, a pixel mapping between the camera and projector images is established. Thirdly, original and inverse adaptive fringes are generated and projected. Finally, 72 wrapped phase calculation equations are given in detail. Experiments confirm that the 3D measurement employing the proposed OIAFPM is much better than that employing AFPM and OIFPM. Although the 3D measurement employing MEM is similar to that employing OIAFPM, the measurement efficiency employing OIAFPM is much higher than that employing MEM.

Diffuse radio source candidate in CIZA J1358.9−4750

AbstractWe report on results of our upgraded Giant Metrewave Radio Telescope (uGMRT) observations for an early-stage merging galaxy cluster, CIZA J1358.9−4750 (CIZA1359), in Band-3 (300–500 MHz). We achieved the image dynamic range of ∼38000 using the direction dependent calibration and found a candidate of diffuse radio emission at 4σrms significance. The flux density of the candidate at 400 MHz, 24.04 ± 2.48 mJy, is significantly positive compared to noise, where its radio power, 2.40 × 1024 W Hz−1, is consistent with those of typical diffuse radio sources of galaxy clusters. The candidate is associated with a part of the X-ray shock front at which the Mach number reaches its maximum value of $\mathcal {M}\sim 1.7$. The spectral index (Fν ∝ να) of the candidate, α = −1.22 ± 0.33, is in agreement with an expected value derived from the standard diffusive shock acceleration (DSA) model. However, such a low Mach number with a short acceleration time would require seed cosmic rays supplied from active galactic nucleus (AGN) activities of member galaxies, as suggested in some other clusters. Indeed, we found seven AGN candidates inside the diffuse source candidate. Assuming the energy equipartition between magnetic fields and cosmic rays, the magnetic field strength of the candidate was estimated to be 2.1 μG. We also find head–tail galaxies and radio phoenixes or fossils near CIZA1359.

Toward very large baseline interferometry observations of black hole structure

Black holes hold a tremendous discovery potential. In this paper the extent to which the Event Horizon Telescope and its next generation upgrade can resolve their structure is quantified. Black holes are characterized by a perfectly absorptive boundary, with a specific area determined by intrinsic parameters of the black hole. We use a general parametrization of spherically symmetric spacetimes describing deviations from this behavior, with parameters controlling the size of the central object and its interaction with light, in particular through a specular reflection coefficient $\Gamma$ and an intrinsic luminosity measured as a fraction $\eta$ of that of the accretion disc. This enables us to study exotic compact objects and compare them with black holes in a model-independent manner. We determine the image features associated with the existence of a surface in the presence of a geometrically thin and optically thick accretion disc, identifying requirements for VLBI observations to be able to cast meaningful constraints on these parameters, in particular regarding angular resolution and image dynamic range. For face-on observations, constraints of order $\eta\lesssim 10^{-4}, \Gamma\lesssim 10^{-1}$ are possible with an enhanced EHT array, imposing strong constraints on the nature of the central object.

Convergent binocular vision algorithm for guiding machining robot under extended imaging dynamic range

The machining quality of large and complex components highly depends on the positioning accuracy of the vision guided robot. In order to solve the problems of limited imaging field-of-view and dynamic range of existing parallel binocular cameras, a convergent binocular vision algorithm under extended imaging dynamic range is proposed to guide the machining robot for precise positioning. Specifically, synthesize high dynamic range images combined with sigmoid function model, and chromaticity space color correction algorithm introduced for adaptive correction of image chromaticity information. The pose between the convergent binocular camera and the robot is calibrated to correct the three-dimensional coordinates of the target workpiece CAD model. The experimental results indicate that the high dynamic images synthesized by the proposed algorithm have clear surface details and balanced colors. This work further enhances the positioning accuracy of the machining robot to the target workpiece which is placed in arbitrary pose within the field-of-view.

P‐3.11: An Improved Compression Method of HDR Image Dynamic Range

P‐3.11: An Improved Compression Method of HDR Image Dynamic Range

Prediction model for perceptual gloss by physical measurement of flat objects

Although a physical gloss exists as a physically measurable index, people can also perceive a perceptual gloss on object surfaces. However, the physical gloss does not always match the perceptual gloss. Because the physical gloss is calculated based only on the specular gloss and does not reflect other physical features that affect the perceptual gloss. Thus, we analysed the relationships between physical features and perceptual gloss by measuring many physical properties of object surfaces, including their physical gloss. We prepared 127 flat objects comprising three materials: paper, resin and metal plating. The perceptual gloss was visually evaluated for observation angles of 20°, 60° and 85° using a magnitude estimation method. Multiple measurements were conducted to obtain physical features such as the gloss unit (GU), haze, distinctness of image and high dynamic range luminance. We then constructed prediction models for the perceptual gloss using these physical features and multiple regression analyses. By combining these multiple physical quantities and using the GU in the power scale, the prediction accuracy was improved. By the optimal power index (0.33 for physical gloss in the common prediction model, independent of the observation angle), we found that human gloss perception may be related to brightness perception.

Adaptive method for image dynamic range adjustment and detail enhancement.

Tone mapping operators (TMOs) aim to adjust high dynamic range (HDR) images to low dynamic range (LDR) ones so that they can be displayed on conventional devices with visual information retained. Nonetheless, existing TMOs can successfully tone-map only limited types of HDR images, and the parameters need to be manually adjusted to yield the best subjective-quality tone-mapped outputs. To cope with the aforementioned issues, an adaptive parameter-free and scene-adaptive TMO for dynamic range adjusting and detail enhancing is proposed to yield a high-resolution and high-subjective-quality tone-mapped output. This method is based on detail/base layer decomposition to decompose the input HDR image into coarse detail, fine detail, and base images. After that, we adopt different strategies to process each layer to adjust the overall brightness and contrast and to retain as much scene information. Finally, a new method, to the best of our knowledge, is proposed for visualization to generate a sequence of artificial images to adjust the brightness. Experiments with numerous HDR images and state-of-the-art TMOs are conducted; the results demonstrate that the proposed method consistently produces better quality tone-mapped images than the state-of-the-art methods.

Dynamic Range Compression Self-Adaption Method for SAR Image Based on Deep Learning

The visualization of synthetic aperture radar (SAR) images involves the mapping of high dynamic range (HDR) amplitude values to gray levels for lower dynamic range (LDR) display devices. This dynamic range compression process determines the visibility of details in the displayed result. It therefore plays a critical role in remote sensing applications. There are some problems with existing methods, such as poor adaptability, detail loss, imbalance between contrast improvement and noise suppression. To effectively obtain the images suitable for human observation and subsequent interpretation, we introduce a novel self-adaptive SAR image dynamic range compression method based on deep learning. Its designed objective is to present the maximal amount of information content in the displayed image and eliminate the contradiction between contrast and noise. Considering that, we propose a decomposition-fusion framework. The input SAR image is rescaled to a certain size and then put into a bilateral feature enhancement module to remap high and low frequency features to realize noise suppression and contrast enhancement. Based on the bilateral features, a feature fusion module is employed for feature integration and optimization to achieve a more precise reconstruction result. Visual and quantitative experiments on synthesized and real-world SAR images show that the proposed method notably realizes visualization which exceeds several statistical methods. It has good adaptability and can improve SAR images’ contrast for interpretation.

Detection of Extended Millimeter Emission in the Host Galaxy of 3C 273 and Its Implications for QSO Feedback via High Dynamic Range ALMA Imaging

We estimate the amount of negative feedback energy injected into the interstellar medium (ISM) of the host galaxy of 3C 273, a prototypical radio-loud quasar. We obtained 93, 233, and 343 GHz continuum images with the Atacama Large Millimeter/submillimeter Array (ALMA). After self-calibration and point-source subtraction, we reach an image dynamic range of ∼85,000 at 93 GHz, ∼39,000 at 233 GHz, and ∼2500 at 343 GHz. These are currently the highest image dynamic range obtained using ALMA. We detect spatially extended millimeter emission associated with the host galaxy, cospatial with the extended emission line region (EELR) observed in the optical. The millimeter spectral energy distribution and comparison with centimeter data show that the extended emission cannot be explained by dust thermal emission or synchrotron or thermal bremsstrahlung arising from massive star formation. We interpret the extended millimeter emission as thermal bremsstrahlung from gas directly ionized by the central source. The extended flux indicates that at least ∼7% of the bolometric flux of the nuclear source was used to ionize atomic hydrogen in the host galaxy. The ionized gas is estimated to be as massive as 1010–1011 M ⊙, but the molecular gas fraction with respect to the stellar mass is consistent with other ellipticals, suggesting that direct ionization ISM by the QSO may not be sufficient to suppress star formation, or we are witnessing a short timescale before negative feedback becomes observable. The discovery of a radio counterpart to EELRs provides a new pathway to studying the QSO–host ISM interaction.

HSVNet: Reconstructing HDR Image from a Single Exposure LDR Image with CNN

Most photographs are low dynamic range (LDR) images that might not perfectly describe the scene as perceived by humans due to the difference in dynamic ranges between photography and natural scenes. High dynamic range (HDR) images have been used widely to depict the natural scene as accurately as possible. Even though HDR images can be generated by an exposure bracketing method or HDR-supported cameras, most photos are still taken as LDR due to annoyance. In this paper, we propose a method that can produce an HDR image from a single arbitrary exposure LDR image. The proposed method, HSVNet, is a deep learning architecture using a Convolutional Neural Networks (CNN) based U-net. Our model uses the HSV color space that enables the network to identify saturated regions and adaptively focus on crucial components. We generated a paired LDR-HDR image dataset of diverse scenes including under/oversaturated regions for training and testing. We also show the effectiveness of our method through experiments, compared to existing methods.

A Vision System With 1-inch 17-Mpixel 1000-fps Block-Controlled Coded-Exposure Stacked-CMOS Image Sensor for Computational Imaging and Adaptive Dynamic Range Control

This study introduces a vision system that can acquire images at high speeds and high resolutions. Image sensors are used not only in digital still cameras but also in various applications that require capturing wide luminance differences beyond human perception. For example, fast, high-resolution object recognition, and motion tracking in automatic driving systems are essential, particularly in dark tunnels or the mid-summer sunshine. However, the resolution, frame rate, pixel size, and dynamic range should be traded off to achieve a high performance in capturing moving objects with a high contrast. We have developed a high-speed vision system with a readout operation of 1000 fps, resolution of 4K <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${\times }\,4\text{K}$ </tex-math></inline-formula> , dynamic range of 110 dB, and fine pixels of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2.7~{\mu }\text{m}$ </tex-math></inline-formula> . These characteristics were achieved using several technologies such as 1) coded exposure (CE), which divides the image plane into smaller blocks and controls the exposure time of each block individually, 2) arrangement of analog-to-digital converters in parallel for each block, and 3) three-dimensional wafer stacking, which enables high-density integration of circuits and pixels. The proposed system can be applied in CE-based computational imaging in addition to high-dynamic-range applications for handling both the dark and bright areas in a scene.

Measurements of charge sharing in a hybrid pixel photon counting CdTe detector

Hybrid pixel radiation detectors working in a single-photon counting mode have gained increasing attention due to their noiseless imaging and high dynamic range. Due to the fact that sensors of different materials can be attached to the readout circuit, they allow operation with a wide range of photon energies. The performance of the single photon counting detectors is limited by pile-up. To allow a detector to work under high flux conditions, the pixel size is reduced, which minimizes detector dead time. However, with smaller pixel sizes the charge sharing effect, a phenomenon that deteriorates both detection efficiency and spatial resolution is more profound.The influence of charge sharing on the detector performance can be quantified using parameterization of the s-curve obtained in the spectral response measurements. The article presents the measurements of the response function of a hybrid pixelated photon counting detector for certain primary energy, which corresponds to the probability of detecting a photon as a function of its energy deposition. The measurements were carried out using an X-ray tube by performing a threshold scan during illumination with X-ray photons of a 1.5 mm and 0.75 mm thick CdTe detector with 100 µm pixel pitch. The charge size cloud depends on the sensor material, the bias voltage, and the sensor thickness. Therefore, the experimental data from a sensor biased with different bias voltages are compared to the theoretical results based on a cascaded model of a single-photon counting segmented silicon detector. The study of the charge sharing influence on the spatial resolution of the CdTe detector will serve for a further study of the possible implementations of the algorithms achieving subpixel resolution, in which the charge sharing becomes the desired effect since the charge division in the pixels is used to interpolate the photon interaction position.

Perceptual Quality Assessment of Low-light Image Enhancement

Low-light image enhancement algorithms (LIEA) can light up images captured in dark or back-lighting conditions. However, LIEA may introduce various distortions such as structure damage, color shift, and noise into the enhanced images. Despite various LIEAs proposed in the literature, few efforts have been made to study the quality evaluation of low-light enhancement. In this article, we make one of the first attempts to investigate the quality assessment problem of low-light image enhancement. To facilitate the study of objective image quality assessment (IQA), we first build a large-scale low-light image enhancement quality (LIEQ) database. The LIEQ database includes 1,000 light-enhanced images, which are generated from 100 low-light images using 10 LIEAs. Rather than evaluating the quality of light-enhanced images directly, which is more difficult, we propose to use the multi-exposure fused (MEF) image and stack-based high dynamic range (HDR) image as a reference and evaluate the quality of low-light enhancement following a full-reference (FR) quality assessment routine. We observe that distortions introduced in low-light enhancement are significantly different from distortions considered in traditional image IQA databases that are well-studied, and the current state-of-the-art FR IQA models are also not suitable for evaluating their quality. Therefore, we propose a new FR low-light image enhancement quality assessment (LIEQA) index by evaluating the image quality from four aspects: luminance enhancement, color rendition, noise evaluation, and structure preserving, which have captured the most key aspects of low-light enhancement. Experimental results on the LIEQ database show that the proposed LIEQA index outperforms the state-of-the-art FR IQA models. LIEQA can act as an evaluator for various low-light enhancement algorithms and systems. To the best of our knowledge, this article is the first of its kind comprehensive low-light image enhancement quality assessment study.

Single-photon peak event detection (SPEED): a computational method for fast photon counting in fluorescence lifetime imaging microscopy.

Fluorescence lifetime imaging microscopy (FLIM) characterizes samples by examining the temporal properties of fluorescence emission, providing useful contrast within samples based on the local physical and biochemical environment of fluorophores. Despite this, FLIM applications have been limited in scope by either poor accuracy or long acquisition times. Here, we present a method for computational single-photon counting of directly sampled time-domain FLIM data that is capable of accurate fluorescence lifetime and intensity measurements while acquiring over 160 Mega-counts-per-second with sub-nanosecond time resolution between consecutive photon counts. We demonstrate that our novel method of Single-photon PEak Event Detection (SPEED) is more accurate than direct pulse sampling and faster than established photon counting FLIM methods. We further show that SPEED can be implemented for imaging and quantifying samples that benefit from higher -throughput and -dynamic range imaging with real-time GPU-accelerated processing and use this capability to examine the NAD(P)H-related metabolic dynamics of apoptosis in human breast cancer cells. Computational methods for photon counting such as SPEED open up more opportunities for fast and accurate FLIM imaging and additionally provide a basis for future innovation into alternative FLIM techniques.