Range Images Research Articles

Enhancing Digital Camera Image Quality and Thermal Efficiency Analysis

Digital camera technology has made significant advances in recent years, enabling the capture of high-resolution images. However, along with the pursuit of image quality, the thermal performance of digital cameras remains a critical consideration, especially in long-term use scenarios or adverse environmental conditions. This is essential in various fields like surveillance, smart cities, etc. This paper explores the application of image overlay algorithms, specifically high dynamic range (HDR) imaging techniques, and examines the impact of thermal efficiency on image quality in digital cameras. Through controlled experiments and analysis, this study aims to improve digital imaging capabilities.

Improved high dynamic range imaging using multi-scale feature flows balanced between task-orientedness and accuracy

Deep learning has made it possible to accurately generate high dynamic range (HDR) images from multiple images taken at different exposure settings, largely owing to advancements in neural network design. However, generating images without artifacts remains difficult, especially in scenes with moving objects. In such cases, issues like color distortion, geometric misalignment, or ghosting can appear. Current state-of-the-art network designs address this by estimating the optical flow between input images to align them better. The parameters for the flow estimation are learned through the primary goal, producing high-quality HDR images. However, we find that this ”task-oriented flow” approach has its drawbacks, especially in minimizing artifacts. To address this, we introduce a new network design and training method that improve the accuracy of flow estimation. This aims to strike a balance between task-oriented flow and accurate flow. Additionally, the network utilizes multi-scale features extracted from the input images for both flow estimation and HDR image reconstruction. Our experiments demonstrate that these two innovations result in HDR images with fewer artifacts and enhanced quality.

Hyperspectral Retinal Imaging as a Non-Invasive Marker to Determine Brain Amyloid Status.

As an extension of the central nervous system (CNS), the retina shares many similarities with the brain and can manifest signs of various neurological diseases, including Alzheimer's disease (AD). To investigate the retinal spectral features and develop a classification model to differentiate individuals with different brain amyloid levels. Sixty-six participants with varying brain amyloid-β protein levels were non-invasively imaged using a hyperspectral retinal camera in the wavelength range of 450-900 nm in 5 nm steps. Multiple retina features from the central and superior views were selected and analyzed to identify their variability among individuals with different brain amyloid loads. The retinal reflectance spectra in the 450-585 nm wavelengths exhibited a significant difference in individuals with increasing brain amyloid. The retinal features in the superior view showed higher inter-subject variability. A classification model was trained to differentiate individuals with varying amyloid levels using the spectra of extracted retinal features. The performance of the spectral classification model was dependent upon retinal features and showed 0.758-0.879 accuracy, 0.718-0.909 sensitivity, 0.764-0.912 specificity, and 0.745-0.891 area under curve for the right eye. This study highlights the spectral variation of retinal features associated with brain amyloid loads. It also demonstrates the feasibility of the retinal hyperspectral imaging technique as a potential method to identify individuals in the preclinical phase of AD as an inexpensive alternative to brain imaging.

Data quality-oriented scan planning for steel structure scenes using a probabilistic genetic algorithm

Scan planning is often challenging particularly in steel structure scenes because of its complex shapes and occlusions. Meeting the requirements of data quality for the scan-to-BIM model is also another issue for accurate point cloud data acquisition. To address these issues, this study proposes a solution that determines an optimal number of scans and corresponding scan positions and parameters. Three primary steps include 1) extraction of feature points using a slicing cutting method and range images, 2) evaluation of data quality using visibility check and data density evaluation, and 3) determination of optimal scan configuration using a probabilistic genetic algorithm. In order to validate the proposed solution, a series of lab-scale experiments involving five case studies with different scenarios are conducted and the results show a similarity of 88.4% between simulation and actual experiments, demonstrating the feasibility of the proposed method for steel structure scenes with complex shapes and occlusions.

Immunology in Practice: a modular framework to support Master of Science students conference attendance and engagement.

Sharing a passion for the advancement of the discipline, the scientific community provides an authentic environment for new members to acquire the knowledge and develop the professional identity needed for their future careers. Supporting opportunities for higher education students to participate in this community can complement their classroom-based education and be extremely beneficial to their learning. Situated in the authentic environment of the scientific community, conferences are organized events where professionals meet to advance their discipline, and which have been shown to provide unique learning opportunities for university students. Here we present a modular framework created to support Imperial College London's Master of Science in Immunology students' attendance at the British Society for Immunology Annual Congress. The module's evaluation indicates an overall students' satisfaction with the content, organization, teaching, assessment, feedback and community aspects of the framework and draws attention to areas of potential improvements. Furthermore, the data emphasize the importance of preconference preparation, of academic mentoring and discusses the role of peer support. Finally, the data highlight the benefits for students of discovering the true breadth and depth of their discipline, of interacting with members of the community and how these contribute to the development of their professional identity.

Visible-short wavelength near infrared hyperspectral imaging coupled with multivariate curve resolution-alternating least squares for diagnosis of breast cancer

This study investigates the application of visible-short wavelength near-infrared hyperspectral imaging (Vis-SWNIR HSI) in the wavelength range of 400–950 nm and advanced chemometric techniques for diagnosing breast cancer (BC). The research involved 56 ex-vivo samples encompassing both cancerous and non-cancerous breast tissue from females. First, HSI images were analyzed using multivariate curve resolution-alternating least squares (MCR-ALS) to exploit pure spatial and spectral profiles of active components. Then, the MCR-ALS resolved spatial profiles were arranged in a new data matrix for exploration and discrimination between benign and cancerous tissue samples using principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA). The PLS-DA classification accuracy of 82.1 % showed the potential of HSI and chemometrics for non-invasive detection of BC. Additionally, the resolved spectral profiles by MCR-ALS can be used to track the changes in the breast tissue during cancer and treatment. It is concluded that the proposed strategy in this work can effectively differentiate between cancerous and non-cancerous breast tissue and pave the way for further studies and potential clinical implementation of this innovative approach, offering a promising avenue for improving early detection and treatment outcomes in BC patients.

Retinochoroidal Vascular Changes in Long-Term Type 1 Diabetic Patients Assessed by Optic Coherence Tomography Angiography.

To study retinal and choriocapillaris (CC) alterations using optical coherence tomography angiography (OCTA) in long-term type 1 diabetic (DM1) patients without diabetic retinopathy (DR). Seventy-eight eyes from 78 well-controlled DM1 patients diagnosed at least 15 years prior and 130 eyes of 130 healthy subjects were included in a cross-sectional descriptive study. Six eyes were excluded from the DM1 group. OCTA with Deep Range Imaging (DRI)-Triton swept source (SS)-OCT was performed. Statistically significant differences were found in all areas of the superficial capillary plexus (SCP), with lower values in DM1 patients. Differences were noted in all quadrants of the deep capillary plexus (DCP) except for the central area. Significant changes in CC blood flow were only found in the center. The foveal avascular zone (FAZ) area and diameters in the SCP were significantly different, while the DCP FAZ area was similar in both groups. Disease duration and microalbuminuria correlated negatively with some SCP areas and positively with FAZ values. Anatomical evaluation revealed microaneurysms in both plexuses, FAZ modifications, and areas lacking blood perfusion. Long-term type 1 diabetic patients without DR display microvascular abnormalities affecting retinal and CC blood perfusion, along with anatomical changes in retinal blood vessels.

Hexagonal Plasmonic Arrays for High-Throughput Multicolor Single-Molecule Studies.

Nanophotonic biosensors offer exceptional sensitivity in the presence of strong background signals by enhancing and confining light in subwavelength volumes. In the field of nanophotonic biosensors, antenna-in-box (AiB) designs consisting of a nanoantenna within a nanoaperture have demonstrated remarkable single-molecule fluorescence detection sensitivities under physiologically relevant conditions. However, their full potential has not yet been exploited as current designs prohibit insightful correlative multicolor single-molecule studies and are limited in terms of throughput. Here, we overcome these constraints by introducing aluminum-based hexagonal close-packed AiB (HCP-AiB) arrays. Our approach enables the parallel readout of over 1000 HCP-AiBs with multicolor single-molecule sensitivity up to micromolar concentrations using an alternating three-color excitation scheme and epi-fluorescence detection. Notably, the high-density HCP-AiB arrays not only enable high-throughput studies at micromolar concentrations but also offer high single-molecule detection probabilities in the nanomolar range. We demonstrate that robust and alignment-free correlative multicolor studies are possible using optical fiducial markers even when imaging in the low millisecond range. These advancements pave the way for the use of HCP-AiB arrays as biosensor architectures for high-throughput multicolor studies on single-molecule dynamics.

Analysis of the ice front propagation in heart tissues: First results

Experimental tests were conducted simultaneously using an infrared (IR) camera and a standard camera in the visible range (VR) to detect the ice front propagation as a function of time and space in different sections of pig heart laid on the surface of a cold spherical aluminum source (30 mm in diameter). The goal of the experiments was to simulate the conditions during the medical practice of internal cryotherapy, which aims to eliminate the tissues responsible for atrial fibrillation in the human heart. Freezing temperatures and related times were also measured, although they were affected by several uncertainties associated with the tests, such as air trapping between the metal sphere and flesh, inhomogeneity of the flesh samples, and the low spatial resolution and accuracy of the IR camera. A comparison with the results of previously conducted numerical simulations (Potenza et al., 2023) [1] is also discussed.

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.

Research and Implementation of FPGA Image Compression Parallel Algorithm Based on CCSDS

High-pixel, high-resolution spectral images are an important source of key graphical information obtained from space remote sensing, however, with the dramatically expanding amount of graphical information, the limited transmission bandwidth and hardware resources are difficult to meet the demand for massive data, which makes real-time image lossless compression and transmission technology an important solution. For this reason, this paper is based on the CCSDS image lossless compression algorithm, using FPGA for the construction of image compression system, and proposes a method strategy based on parallel acceleration algorithm to realize the real time processing of large-capacity hyperspectral images. The simulation results show that hyperspectral river and mountain range images can be compressed with greatly improved compression efficiency while maintaining real time performance and accuracy.

Imaging method of surface defect using leaky Rayleigh wave with ultrasonic phased array

ABSTRACT Leaky Rayleigh wave testing has the advantages of non-contact, high sensitivity, and good beam directivity. However, current leaky Rayleigh wave testing mostly uses monolithic transducers and is seriously affected by acoustic wave attenuation and diffusion, resulting in poor image quality, limited detection range and low imaging efficiency. In this paper, a novel leaky Rayleigh wave testing method using an ultrasonic phased array is developed, and the total focusing method (TFM) is used to achieve high-quality imaging of defects. To improve imaging efficiency, the root-mean-square velocity (RMSV) model is utilised to rapidly acquire an approximate analytical solution for the time of flight of the acoustic wave. To obtain more accurate defect images further, the amplitude of the total focusing imaging signal is calibrated. The experimental results demonstrate that the proposed method achieves a 48% improvement in the imaging accuracy of a 1 mm hole, compared to the traditional Synthetic Aperture Focusing Technique (SAFT) imaging method. The proposed method achieves an 92% reduction in imaging time compared to the conventional TFM. The proposed method provides a new non-contact approach for high-accuracy and high-efficiency defect imaging using leaky Rayleigh wave.

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.

Weather Radar Calibration Method Based on UAV-Suspended Metal Sphere.

Weather radar is an active remote sensing device used to monitor the full lifecycle changes in severe convective weather with high spatial and temporal resolution. Effective radar calibration is a crucial foundation for ensuring the high-quality application of observational data. This paper utilizes a UAV platform equipped with a high-precision RTK system and standard metal spheres to study the principles and methods of metal sphere calibration, constructing a complete calibration process and calibration accuracy evaluation metrics. Additionally, a collocated radar comparison observation experiment was conducted for cross-validation, and metal sphere calibration tests were performed on problematic radars. The experimental results indicate the following: (1) The combined application of a high-precision RTK system and a laser range camera can provide real-time position information on the metal sphere, improving the efficiency of radar target acquisition. (2) The calibration method based on UAV-suspended metal spheres can periodically conduct the quantitative calibration of Z and ZDR, achieving calibration accuracies within 0.5 dB and 0.2 dB, respectively, and supports the qualitative inspection of key parameters such as beamwidth and pulse width. (3) During field tests, a high success rate "coarse adjustment + fine adjustment + staring" sphere-finding technique was established, based on automatic switching between RHI, PPI, and FIX scanning modes. This method directs the UAV to adjust the metal sphere to the center of the radar distance bin, reducing the impact of uneven beam filling and bin crossing, ensuring the accuracy of scattering characteristic measurements.

MetaHDR: single shot high-dynamic range imaging and sensing using a multifunctional metasurface

We present MetaHDR, which is a single-shot high-dynamic range (HDR) imaging and sensing system using a multifunctional metasurface. The metasurface is capable of splitting an incident beam into multiple focusing beams with different amounts of power, simultaneously forming multiple low dynamic range (LDR) images with distinct irradiance on a photosensor. Then, the LDR images are jointly processed using a gradient-based HDR fusion algorithm, which is shown to be effective in attenuating the residual light artifacts incurred by the metasurface and the lens flare. MetaHDR achieves single-shot HDR photography and videography that increases the dynamic range by at least 50 dB compared to the original dynamic range of the photosensor. It can also perform single-shot HDR sensing, including reflectance calibration and surface curvature estimation of reflective materials. MetaHDR’s demonstrated functionalities could be broadly applied in surveillance and security, microscopic imaging, advanced manufacturing, etc.

Four-Dimensional Digital Monitoring and Registering of Historical Architecture for the Preservation of Cultural Heritage

Preserving cultural heritage through monitoring, registering, and analyzing damage in historical architectural structures presents significant financial and logistical burdens. Developed approaches for monitoring and registering 4D (4-dimensional)-scanned range and raster images of damaged objects were investigated in a case study of historical Baron Palace in Egypt. In the methodology, we first prepared and observed the damaged historical models. The damaged historical models were scanned using a laser scanner at a predetermined date and time. Simultaneously, digital images of the models were captured (by a calibrated digital camera) and stored on a researcher’s tablet device. By observing and comparing the scanned models with the digital images, geometric defects and their extent are identified. Then, the observed data components were detected on the map. Then, damaged statue materials were investigated using system of energy dispersive (SEM; scanning electron microscope, Gemini Zeiss-Ultra 55) and XRF (X-ray fluorescence) spectroscopic analysis to identify the statue’s marble elements, and the results indicate that SEM-EDX and XRF analyses accurately identify major and minor compositions of the damaged statue. Then, the damaged models were registered in two stages. In the registration stages, the corresponding points were determined automatically by detecting the closest points in the clouds and ICP (iterative closest point) algorithm in RiSCAN. The point clouds (of the Palace and damaged statues) gave very detailed resolutions and more realistic images in RiSCAN, but it is a costly program. Finally, the accuracies of the registration tasks were assessed; the standard deviations are within acceptable limits and tend to increase irregularly as the number of polydata observations used in the registration calculations increase.

Generalizing event-based HDR imaging to various exposures

Single-exposure High Dynamic Range Imaging (HDRI), as a typical ill-posed problem, has attracted extensive attention from researchers. However, restoration in real-world scenarios has always been an intractable task due to various exposures and noise artifacts. This work proposes an event-based HDRI framework that generalizes to scenes under various exposures by exploiting the high dynamic range of events. To address the challenge of processing diverse exposures, we propose an exposure-aware network incorporating the exposure attention fusion module, which facilitates the adaptive fusion of SDR image and event features. Moreover, the problem of noise in extremely under-exposed regions and events is effectively alleviated by introducing a self-supervised loss, namely EDDN, which effectively enhances the details of saturated areas while simultaneously decreasing noise. We conduct novel event-based HDRI datasets to evaluate our proposed method for benchmarking with diverse exposed images. Comprehensive experiments have demonstrated that our method outperforms the state-of-the-art.

Multi-stage coarse-to-fine progressive enhancement network for single-image HDR reconstruction

Compared with traditional imaging, high dynamic range (HDR) imaging technology can record scene information more accurately, thereby providing users higher quality of visual experience. Inverse tone mapping is a direct and effective way to realize single-image HDR reconstruction, but it usually suffers from some problems such as detail loss, color deviation and artifacts. To solve the problems, this paper proposes a multi-stage coarse-to-fine progressive enhancement network (named MSPENet) for single-image HDR reconstruction. The entire multi-stage network architecture is designed in a progressive manner to obtain higher-quality HDR images from coarse-to-fine, where a mask mechanism is used to eliminate the effects of over-exposure regions. Specifically, in the first two stages, two asymmetric U-Nets are constructed to learn the multi-scale information of input image and perform coarse reconstruction. In the third stage, a residual network with channel attention mechanism is constructed to learn the fusion of progressively transferred multi-level features and perform fine reconstruction. In addition, a multi-stage progressive detail enhancement mechanism is designed, including progressive gated recurrent unit fusion mechanism and multi-stage feature transfer mechanism. The former fuses the progressively transferred features with coarse HDR features to reduce the error stacking effect caused by multi-stage networks. Meanwhile, the latter fuses early features to supplement the lost information during each stage of feature delivery and combines features from different stages. Extensive experimental results show that the proposed method can reconstruct higher quality HDR images and effectively recover texture and color information in over-exposure regions compared to the state-of-the-art methods.