High Dynamic Range Research Articles

Cinematic Gaussians: Real‐Time HDR Radiance Fields with Depth of Field

AbstractRadiance field methods represent the state of the art in reconstructing complex scenes from multi‐view photos. However, these reconstructions often suffer from one or both of the following limitations: First, they typically represent scenes in low dynamic range (LDR), which restricts their use to evenly lit environments and hinders immersive viewing experiences. Secondly, their reliance on a pinhole camera model, assuming all scene elements are in focus in the input images, presents practical challenges and complicates refocusing during novel‐view synthesis. Addressing these limitations, we present a lightweight method based on 3D Gaussian Splatting that utilizes multi‐view LDR images of a scene with varying exposure times, apertures, and focus distances as input to reconstruct a high‐dynamic‐range (HDR) radiance field. By incorporating analytical convolutions of Gaussians based on a thin‐lens camera model as well as a tonemapping module, our reconstructions enable the rendering of HDR content with flexible refocusing capabilities. We demonstrate that our combined treatment of HDR and depth of field facilitates real‐time cinematic rendering, outperforming the state of the art.

Imaging system high dynamic range colorimetric calibration method based on a digital chain

Research on high dynamic range (HDR) color management imposes critical requirements on calibration methods between imaging systems and standard radiation. This paper proposes a colorimetric calibration method based on digital chain measurement for imaging systems. First, a HDR colorimetric calibration process model for imaging systems is constructed based on an imaging chain. It includes a light source, target reflectance, optical system parameters, spectral sensitivities, and color matching functions. Subsequently, visual tristimulus values and three-channel response values of an imaging system are obtained using the proposed model in response to the same target, and the target characteristic parameters are adjusted to simulate different HDR imaging scenarios. Following that, various regression algorithms can be employed for HDR colorimetric calibration of imaging systems. The experimental findings demonstrate that the method proposed in this paper boasts a broader dynamic range and denser sampling, thereby enhancing the accuracy of colorimetric characterization models and achieving superior resolution in color measurement.

CGLight: An effective indoor illumination estimation method based on improved convmixer and GauGAN

Illumination consistency is a key factor for seamlessly integrating virtual objects with real scenes in augmented reality (AR) systems. High dynamic range (HDR) panoramic images are widely used to estimate scene lighting accurately. However, generating environment maps requires complex deep network architectures, which cannot operate on devices with limited memory space. To address this issue, this paper proposes CGLight, an effective illumination estimation method that predicts HDR panoramic environment maps from a single limited field-of-view (LFOV) image. We first design a CMAtten encoder to extract features from input images, which learns the spherical harmonic (SH) lighting representation with fewer model parameters. Guided by the lighting parameters, we train a generative adversarial network (GAN) to generate HDR environment maps. In addition, to enrich lighting details and reduce training time, we specifically introduce the color consistency loss and independent discriminator, considering the impact of color properties on the lighting estimation task while improving computational efficiency. Furthermore, the effectiveness of CGLight is verified by relighting virtual objects using the predicted environment maps, and the root mean square error and angular error are 0.0494 and 4.0607 in the gray diffuse sphere, respectively. Extensive experiments and analyses demonstrate that CGLight achieves a balance between indoor illumination estimation accuracy and resource efficiency, attaining higher accuracy with nearly 4 times fewer model parameters than the ViT-B16 model.

126 dB High Dynamic Range SPAD Frontend for Time of Flight Applications

126 dB High Dynamic Range SPAD Frontend for Time of Flight Applications

Leather-Based Shoe Soles for Real-Time Gait Recognition and Automatic Remote Assistance Using Machine Learning.

Real-time monitoring of gait characteristics is crucial for applications in health monitoring, patient rehabilitation feedback, and telemedicine. However, the effective and stable acquisition and automatic analysis of gait information remain significant challenges. In this study, we present a flexible sensor based on a carbon nanotube/graphene composite conductive leather (CGL), which uses collagen fiber with a three-dimensional network structure as the flexible substrate. The CGL-based sensor demonstrates a high dynamic range, with notable pressure responses ranging from 0.6 to 14.5 kPa and high sensitivity (S = 0.2465 kPa-1). We further developed a device incorporating the CGL-based sensor to collect foot characteristic signals from human motion and designed smart sports shoes to facilitate effective human-computer interaction. Machine learning was employed to collect and process gait characteristic information in various states, including standing, sitting, walking, and falling. For real-time monitoring of falls, we optimized the K-Nearest Time Series Classifier (KNTC) algorithm, achieving an accuracy of 0.99 and a prediction time of only 13 ms, which highlights the system's excellent intelligent response capabilities. The system maintained a gait recognition accuracy of 90% across diverse populations, with low false-positive (3.3%) and false-negative (3.3%) rates. This work demonstrates stable gait recognition capabilities and provides valuable methods and insights for plantar behavior monitoring and data analysis, contributing to the development of advanced real-time gait monitoring systems.

Predictive pixel-wise optical encoding: towards single-shot high dynamic range moving object recognition

Conventional low dynamic range (LDR) imaging devices fail to preserve much information for further vision tasks because of the saturation effect. Thus, high dynamic range (HDR) imaging is an important imaging technology in extreme illuminance conditions, which enables a wide range of applications, including photography, autonomous driving, and robotics. Mainstream approaches require multi-shot methods because the conventional camera can only control the exposure globally. Although they perform well on static HDR imaging, they face a challenge with real-time HDR imaging for motional scenes because of the artifact and time latency caused by multi-shot and post-processing. To this end, we propose a framework, termed POE-VP, which achieves single-shot HDR imaging via a pixel-wise optical encoder driven by video prediction. We use highlighted motional license plate recognition as a downstream vision task to demonstrate the performance of POE-VP. From the results of simulation and real scene experiments, we validate that POE-VP outperforms conventional LDR cameras by more than 5 times in recognition accuracy and by more than 200% in information entropy. The dynamic range could reach 120 dB, and the captured data size is verified to be lower than mainstream multi-shot methods by 67%. The running time of POE-VP is also validated to satisfy the needs of high-speed HDR imaging.

Anisotropic Specular Image‐Based Lighting Based on BRDF Major Axis Sampling

AbstractAnisotropic specular appearances are ubiquitous in the environment: brushed stainless steel pans, kettles, elevator walls, fur, or scratched plastics. Real‐time rendering of these materials with image‐based lighting is challenging due to the complex shape of the bidirectional reflectance distribution function (BRDF). We propose an anisotropic specular image‐based lighting method that can serve as a drop‐in replacement for the standard bent normal technique [Rev11]. Our method yields more realistic results with a 50% increase in computation time of the previous technique, using the same high dynamic range (HDR) preintegrated environment image. We use several environment samples positioned along the major axis of the specular microfacet BRDF. We derive an analytic formula to determine the two closest and two farthest points from the reflected direction on an approximation of the BRDF confidence region boundary. The two farthest points define the BRDF major axis, while the two closest points are used to approximate the BRDF width. The environment level of detail is derived from the BRDF width and the distance between the samples. We extensively compare our method with the bent normal technique and the ground truth using the GGX specular BRDF.

Research on Color Management and Color Grading Applications in New Media Movies

This article studies the color management and color grading of new media movies, mainly studying the impact of color balance, stylized color grading and high dynamic range image adjustment on the visual effects and emotional expression of the film. However, there are currently not many specifications and requirements for the color requirements of new media movies. New media movies still have deficiencies in color aesthetics and artistry. They often pursue visual stimulation and alternative themes while ignoring artistic expression. Due to limitations in cost and production experience, online movies often have less investment in color aesthetics, and the production level is still far behind traditional movies. The research significance of this article is to emphasize the importance of color management and color correction technology in new media movies, and to propose solutions to color inconsistency problems caused by color management and cross-platform playback. Through scientific color management and delivery settings, new media movies can be ensured to present the best visual effects on different devices, improving the artistic value and enjoyment of new media movies. In general, the research in this article has reference significance for the standardization development and technological improvement of the new media film industry.

Reconstructing High Dynamic Range Image from a Single Low Dynamic Range Image Using Histogram Learning

High dynamic range imaging is an important field in computer vision. Compared with general low dynamic range (LDR) images, high dynamic range (HDR) images represent a larger luminance range, making the images closer to the real scene. In this paper, we propose an approach for HDR image reconstruction from a single LDR image based on histogram learning. First, the dynamic range of an LDR image is expanded to an extended dynamic range (EDR) image. Then, histogram learning is established to predict the intensity distribution of an HDR image of the EDR image. Next, we use histogram matching to reallocate pixel intensities. The final HDR image is generated through regional adjustment using reinforcement learning. By decomposing low-frequency and high-frequency information, the proposed network can predict the lost high-frequency details while expanding the intensity ranges. We conduct the experiments based on HDR-Real and HDR-EYE datasets. The quantitative and qualitative evaluations have demonstrated the effectiveness of the proposed approach compared to the previous methods.

High dynamic range mapping for the evaluation of parallel transmit arrays.

Demonstration of a high dynamic-range and high SNR method for acquiring absolute maps from a combination of gradient echo and actual-flip-angle measurements that is especially useful during the construction of parallel-transmit arrays. Low flip angle gradient echo images, acquired when transmitting with each channel individually, are used to compute relative maps. Instead of computing these in a conventional manner, the equivalence of the problem to the ESPIRiT parallel image reconstruction method is used to compute maps with a higher SNR. Absolute maps are generated by calibration against a single actual flip-angle acquisition when transmitting on all channels simultaneously. Depending on the number of receiver channels and the location of the receive elements with respect to the subject being investigated, moderate to high gains in the SNR of the acquired maps can be achieved. The proposed method is especially suited for the acquisition of maps during the construction of transceiver arrays. Compared to the original method, maps with higher SNR can be computed without the need for additional measurements, and maps can also be generated using previously acquired data. Furthermore, easy adoption and fast estimation of receiver channels is possible because of existing highly optimized open-source implementations of ESPIRiT, such as in the BART toolbox.

Mini-LED Backlight: Advances and Future Perspectives

Miniaturized-light-emitting diode (mini-LED) backlights have emerged as the state-of-the-art technology for liquid crystal display (LCD), facilitating the improvement in a high dynamic range (HDR) and power saving. The local dimming technology divides the backlight into several dimming zones. Employing mini-LEDs, whose size ranges from 100 to 200 μm, as the light sources can enlarge the number of zones in the local dimming backlight, fulfilling the requirement for HDR. However, the halo effect still acts as one of the primary technological bottlenecks for mini-LED backlights. In this review, packaging technology of LEDs, color conversion, and the driving scheme of mini-LED backlights have been discussed. The strategies to reduce optical crosstalk in adjacent areas by various improved optical structures or to suppress the halo effect of LCDs by mini-LED backlights are summarized. The development trends of mini-LED backlights are also discussed.

Real-time volume rendering for three-dimensional fetal ultrasound using volumetric photon mapping

Three-dimensional (3D) fetal ultrasound has been widely used in prenatal examinations. Realistic and real-time volumetric ultrasound volume rendering can enhance the effectiveness of diagnoses and assist obstetricians and pregnant mothers in communicating. However, this remains a challenging task because (1) there is a large amount of speckle noise in ultrasound images and (2) ultrasound images usually have low contrasts, making it difficult to distinguish different tissues and organs. However, traditional local-illumination-based methods do not achieve satisfactory results. This real-time requirement makes the task increasingly challenging. This study presents a novel real-time volume-rendering method equipped with a global illumination model for 3D fetal ultrasound visualization. This method can render direct illumination and indirect illumination separately by calculating single scattering and multiple scattering radiances, respectively. The indirect illumination effect was simulated using volumetric photon mapping. Calculating each photon’s brightness is proposed using a novel screen-space destiny estimation to avoid complicated storage structures and accelerate computation. This study proposes a high dynamic range approach to address the issue of fetal skin with a dynamic range exceeding that of the display device. Experiments show that our technology, compared to conventional methodologies, can generate realistic rendering results with far more depth information.

Solid, structured composite neutron detectors with high dynamic range capability

Neutron detectors are essential across disciplines such as fundamental science, nuclear security, safeguards, and civilian applications. While 3He-filled gas proportional counters have long been revered for their efficacy in detecting thermal neutrons and praised for their efficiency, neutron/gamma discrimination, and stability, the scarcity of 3He has spurred a search for alternatives. Here, we explore a solid structured scintillating particle composite (SPC) consisting of 6Li-containing scintillating glass particles within an acrylic matrix as a neutron detector for high dynamic range applications. We show for the first time that an SPC neutron detector can boast an intrinsic detection efficiency of 0.261% for pure 252Cf fission neutrons and an overall neutron detection efficiency of (0.546 ± 0.003)% at the Neutron Free-in-Air facility while being able to function in an intense gamma-ray environment. We also show that the SPC neutron detector supports fast neutron capture times and enables a dual-readout scheme that extends the detector dynamic range to high incident neutron fluxes. A scalable fabrication process allows for tailoring the SPC detector properties to the requirements of specific applications. Good agreement is found between the experimental results taken with a National Institute of Standards and Technology traceable 252Cf source and the coupled MCNP6 and optical-ray-tracing simulations.

Asynchronous Visual-Inertial Odometry for Event Cameras

Abstract. Event cameras are bio-inspired visual sensors that output changes in pixel-level brightness asynchronously instead of standard intensity frames at a fixed rate. These cameras offer reliable visual information in high-speed motion and high dynamic range (HDR) scenes, addressing the limitations of traditional cameras in such scenarios. Therefore, research of integrating event cameras into established visual algorithms holds significant value. In this study, based on traditional Visual-Inertial Odometry (VIO) frameworks, we proposed an innovative asynchronous monocular event-based inertial odometry method to fully exploit the benefits of event cameras. First, the corner features are extracted separately from the raw event stream and the time surface map, followed by uniform feature selection to accurately describe three-dimensional spatial geometry. Then, feature tracking is achieved by integrating these two event representation methods. In addition, our method obtains stable and high frequency state estimation by fusing event and IMU measurements through graph optimization. We validate the effectiveness of our proposed approach, comparing with several state-of-the-art EVIO systems and VIO systems.

Multi exposure fusion for high dynamic range imaging via multi-channel gradient tensor

Multi-exposure fusion (MEF) is an effective technique for directly fusing a sequence of low dynamic range (LDR) images from a high dynamic range (HDR) natural scene. The goal is to generate an information enriched LDR image. Despite its effectiveness, current MEF methods often encounter issues such as detail loss and color degradation. Additionally, existing algorithms often struggle to balance image quality and computation time, particularly for large-sized images. This paper introduces an innovative MEF algorithm that address these challenges, offering improved performance and computational time across all image sizes. The algorithm employs a multi-channel gradient tensor on RGB images to effectively capture the contrast information among the three channels. This mechanism allows an edge-preserving image filter to maintain edges while smoothing weight maps. To enhance computational efficiency, the algorithm uses a fast approximation method suitable for large sized images. Our comprehensive experimental results demonstrate that the proposed method outperforms existing MEF techniques both quantitatively and qualitatively. Furthermore, our method reduces computational time by approximately 30% compared to the most recent state-of-the-art techniques.

How Many Events are Needed for One Reconstructed Image Using an Event Camera?

Abstract. Event cameras offer significant advantages over traditional cameras, including high temporal resolution, high dynamic range, and low power consumption. However, reconstructing images from the asynchronous events generated by these cameras presents unique challenges. This paper investigates the optimal number of events needed for high-quality image reconstruction using event cameras. We evaluate two primary reconstruction strategies—fixed time window and fixed number of events—across various dynamic and static scenes. Our study includes scenarios with different lighting conditions and camera movements. Using the state-of-the-art E2VID algorithm, we perform both qualitative and quantitative analyses of the reconstructed images, comparing them with reference frames from a traditional RGB camera. Our results demonstrate the trade-offs between temporal resolution and image quality for each reconstruction strategy, providing insights into the optimal settings for different applications. This research offers practical guidelines for selecting appropriate reconstruction parameters to achieve the better image quality from event cameras.

Multiexposed Image-Fusion Strategy Using Mutual Image Translation Learning with Multiscale Surround Switching Maps

The dynamic range of an image represents the difference between its darkest and brightest areas, a crucial concept in digital image processing and computer vision. Despite display technology advancements, replicating the broad dynamic range of the human visual system remains challenging, necessitating high dynamic range (HDR) synthesis, combining multiple low dynamic range images captured at contrasting exposure levels to generate a single HDR image that integrates the optimal exposure regions. Recent deep learning advancements have introduced innovative approaches to HDR generation, with the cycle-consistent generative adversarial network (CycleGAN) gaining attention due to its robustness against domain shifts and ability to preserve content style while enhancing image quality. However, traditional CycleGAN methods often rely on unpaired datasets, limiting their capacity for detail preservation. This study proposes an improved model by incorporating a switching map (SMap) as an additional channel in the CycleGAN generator using paired datasets. The SMap focuses on essential regions, guiding weighted learning to minimize the loss of detail during synthesis. Using translated images to estimate the middle exposure integrates these images into HDR synthesis, reducing unnatural transitions and halo artifacts that could occur at boundaries between various exposures. The multilayered application of the retinex algorithm captures exposure variations, achieving natural and detailed tone mapping. The proposed mutual image translation module extends CycleGAN, demonstrating superior performance in multiexposure fusion and image translation, significantly enhancing HDR image quality. The image quality evaluation indices used are CPBDM, JNBM, LPC-SI, S3, JPEG_2000, and SSEQ, and the proposed model exhibits superior performance compared to existing methods, recording average scores of 0.6196, 15.4142, 0.9642, 0.2838, 80.239, and 25.054, respectively. Therefore, based on qualitative and quantitative results, this study demonstrates the superiority of the proposed model.

High dynamic range land wavefield reconstruction from randomized acquisition

Compressive sensing (CS) is an alternative to regular Shannon sampling that captures similar information from reduced measurements. It relies on randomized sampling patterns and a sparse data representation to reconstruct the regularly sampled object. CS is an important ingredient in affordable seismic acquisition, which can lead to improvements in the near-surface mapping and noise suppression for land data. However, the near surface traps most of the source-generated energy, resulting in data that are rich in high-wavenumber content and have amplitudes spanning several orders of magnitude. When dealing with such high dynamic range nonstationary data, the Fourier domain is not optimal for providing a sparse representation — a necessary condition for successful application of CS. In contrast, a discrete complex wavelet transform can localize high-energy features, has good directional selectivity, and is near-shift invariant. Combined, these properties allow complex wavelets to represent detail-rich wavefields in a compact form. To leverage these features and achieve good CS reconstructions, we develop a scale- and orientation-dependent iterative soft thresholding (IST) scheme for reconstructing high dynamic range wavefields. Our approach requires little parameterization, is easy to implement, and is robust to reconstructed wavefield sampling grid and dynamic range. We test IST on different wavefields with randomly missing traces and compare the data reconstructions with the spectral projected gradient solver and projection onto convex sets. We quantify the reconstructions by a direct comparison of Fourier coefficients between fully sampled and reconstructed wavefields. Taking log10 of Fourier coefficients prior to computing the quality metric deemphasizes the importance of magnitude match while highlighting Fourier coefficient support accuracy, which usually translates into good structural fidelity of reconstructed data. We find that IST performs consistently among all examples, yielding good phase match while performing gentle denoising.

HDR-CNF: single-image high dynamic range imaging based on conditional normalizing flows

HDR-CNF: single-image high dynamic range imaging based on conditional normalizing flows

Multi-view high-dynamic-range 3D reconstruction and point cloud quality evaluation based on dual-frame difference images

A high dynamic range can easily lead to image saturation, making it a challenge for structured light 3D reconstruction. The article proposes a multi-view 3D topography measurement system, which consists of dual projectors, a single camera, and a high-precision rotary platform. The system utilizes single-frame images to achieve high-dynamic-range surface adaptive exposure. Subsequently, it proposes a method that combines two-frame differential images with multi-view imaging to identify highly reflective regions and complete point cloud holes. This approach addresses the issue of visual blind spots caused by shadows and local high reflectivity due to the occlusion of the measured object’s geometric features. Finally, the system employs deep learning to evaluate the quality of the high-dynamic-range point cloud data. Comparative experiments show that optimal exposure in single-frame images can achieve better imaging quality and shorten capture time. The dual-frame difference image algorithm can identify high-reflection areas and complete the point cloud data. The point cloud quality evaluation model based on IT-PCQA demonstrates the effectiveness of the proposed method for high-dynamic-range 3D reconstruction.