Camera Exposure Research Articles

Imaging mitochondria through bone in live mice using two-photon fluorescence microscopy with adaptive optics.

Mitochondria are extremely important organelles in the regulation of bone marrow and brain activity. However, live imaging of these subcellular features with high resolution in scattering tissues like brain or bone has proven challenging. In this study, we developed a two-photon fluorescence microscope with adaptive optics (TPFM-AO) for high-resolution imaging, which uses a home-built Shack-Hartmann wavefront sensor (SHWFS) to correct system aberrations and a sensorless approach for correcting low order tissue aberrations. Using AO increases the fluorescence intensity of the point spread function (PSF) and achieves fast imaging of subcellular organelles with 400 nm resolution through 85 μm of highly scattering tissue. We achieved ~1.55×, ~3.58×, and ~1.77× intensity increases using AO, and a reduction of the PSF width by ~0.83×, ~0.74×, and ~0.9× at the depths of 0, 50 μm and 85 μm in living mouse bone marrow respectively, allowing us to characterize mitochondrial health and the survival of functioning cells with a field of view of 67.5× 67.5 μm. We also investigate the role of initial signal and background levels in sample correction quality by varying the laser power and camera exposure time and develop an intensity-based criteria for sample correction. This study demonstrates a promising tool for imaging of mitochondria and other organelles in optically distorting biological environments, which could facilitate the study of a variety of diseases connected to mitochondrial morphology and activity in a range of biological tissues.

Analysis of the Accuracy Potential of a Stereo High-Speed Camera System in 3D Measurements in Highly Dynamic Experiments.

In the context of setting up a stereo high-speed camera system for accurate 3D measurements in highly dynamic experiments, the potential of a "Fastcam SA-X2" stereo system is evaluated by testing different camera configurations and motion scenarios. A thorough accuracy analysis is performed using spatial rigid-body transformations and relative measurement analyses of photogrammetrically reconstructed surfaces of nondeformable objects. The effects of camera calibration, exposure time, object velocity, and object surface pattern quality on the quality of adjusted 3D coordinates are taken into consideration. While the exposure time does not significantly influence the quality of the static measurements, the results of dynamic experiments demonstrate that not only an insufficient frame rate but also an increased noise level resulting from short exposure times affects 3D coordinate accuracy. Using appropriate configurations to capture dynamic events, the errors in dynamic experiments do not differ significantly from the errors obtained in static measurements. A spatial mapping error of less than 1 μm is obtained through the experiments, with proper testing configurations for an object surface area of 5×20 mm. These findings are relevant for users of high-speed stereo imaging techniques to perform geometric 3D measurements, deformation, and crack analyses.

FT‐LVIO: Fully Tightly coupled LiDAR‐Visual‐Inertial odometry

AbstractIn this paper, we propose a fully tightly‐coupled multi‐sensor fusion framework termed FT‐LVIO, that fuses measurements from a light detection and ranging (LiDAR), a monocular camera and an inertial measurement unit (IMU) simultaneously to achieve robust and accurate state estimation in real time. FT‐LVIO is built atop the framework of an error‐state‐iterated Kalman filter. To take full advantage of the complimentary characteristics of individual sensors, LiDAR point clouds are undistorted by IMU prediction to the nearest camera exposure time and the filter is updated with measurements from all sensors. In addition, an efficient sampling method for the LiDAR point‐to‐plane measurements is proposed, which can help select the measurements providing sufficient constraints to the pose estimation and facilitate a low‐drift odometry. Extensive experiments are performed on both the public NTU dataset and the private handheld dataset, and the results show that the proposed FT‐LVIO outperforms the state‐of‐the‐art LiDAR‐inertial, visual‐inertial and LiDAR‐visual‐inertial methods in both accuracy and robustness. Furthermore, FT‐LVIO can survive in the challenging staircase environment.

Digital camera obscuras

A camera obscura is a darkened chamber in which an image of the scene outside the chamber is projected by a pinhole or other optic onto a screen within the chamber. Early obscuras used pinhole optics, but by the 16th century obscuras with lenses became popular as aids for drawing or painting scenes with the correct perspective. By the late 19th century, the screen had largely been replaced with photo-sensitive materials, and film cameras replaced obscuras. Over the last few decades, digital cameras using electronic sensors have replaced those using film. However, large projections can have significantly different properties from small projections, and it is very difficult to build a large digital image sensor. Thus, there is interest in using a small-sensor digital camera to photograph the large screen of a obscura. For example, it is relatively easy to obtain much shallower depth of field using a large screen, but a small sensor photographing the screen essentially copies that depth of field, so obscuras have often been used as “bokeh adapters” for small-sensor digital cameras. The current work is an experimentally-grounded exploration of the issues that arise in construction of digital camera obscuras, their use, and exposure control and postprocessing of digital images captured using a small sensor to photograph the image projected on an obscura’s screen.

ТЕХНИЧЕСКАЯ ЗАЩИТА ВИЗУАЛЬНОЙ ИНФОРМАЦИИ В ОФИСНЫХ ПОМЕЩЕНИЯХ МЕТОДОМ ИНФРАКРАСНОЙ ЗАСВЕТКИ

The paper presents the results of qualitative theoretical analysis and experimental researches of the influence of infrared radiation (illumination) on a webcam operation. The video cameras used in hidden surveillance are made on the basis of silicon CCD-matrix. The sensitivity of silicon CCDmatrix to optical radiation exceeds the sensitivity of the human eye by several times, and it is shifted along the spectrum to the infrared region not visible to the eye. Controlling the exposure and aperture of the video camera allows changing the sensitivity of the camera within a wide range, adjusting to changes in external illumination. Infrared radiation invisible to the eye has a significant effect on the formation of the recorded video image, and it cannot be completely cut off without a significant deterioration in the characteristics of video shooting. The formation of the uniform and isotropic illumination in the infrared region in the room can neutralize any covert surveillance based on video cameras. Researches have shown the possibility of destructive effects of the infrared radiation on video shooting without destroying electronics in the camera, which does not affect the comfort of employees in a protected room. The authors propose methods of protecting office premises from hidden video surveillance and a methodology for studying the effects of infrared radiation on a webcam in the course of the physical basics of information protection.

Image Quality Enhancement with Applications to Unmanned Aerial Vehicle Obstacle Detection

Aiming at the problem that obstacle avoidance of unmanned aerial vehicles (UAVs) cannot effectively detect obstacles under low illumination, this research proposes an enhancement algorithm for low-light airborne images, which is based on the camera response model and Retinex theory. Firstly, the mathematical model of low-illumination image enhancement is established, and the relationship between the camera response function (CRF) and brightness transfer function (BTF) is constructed by a common parameter equation. Secondly, to solve the problem that the enhancement algorithm using the camera response model will lead to blurred image details, Retinex theory is introduced into the camera response model to design an enhancement algorithm framework suitable for UAV obstacle avoidance. Thirdly, to shorten the time consumption of the algorithm, an acceleration solver is adopted to calculate the illumination map, and the exposure matrix is further calculated via the illumination map. Additionally, the maximum exposure value is set for low signal-to-noise ratio (SNR) pixels to suppress noise. Finally, a camera response model and exposure matrix are used to adjust the low-light image to obtain an enhanced image. The enhancement experiment for the constructed dataset shows that the proposed algorithm can significantly enhance the brightness of low-illumination images, and is superior to other similar available algorithms in quantitative evaluation metrics. Compared with the illumination enhancement algorithm based on infrared and visible image fusion, the proposed algorithm can achieve illumination enhancement without introducing additional airborne sensors. The obstacle object detection experiment shows that the proposed algorithm can increase the AP (average precision) value by 0.556.

Enhancement of Imaging Quality of Interferenceless Coded Aperture Correlation Holography Based on Physics-Informed Deep Learning

Interferenceless coded aperture correlation holography (I-COACH) was recently introduced for recording incoherent holograms without two-wave interference. In I-COACH, the light radiated from an object is modulated by a pseudo-randomly-coded phase mask and recorded as a hologram by a digital camera without interfering with any other beams. The image reconstruction is conducted by correlating the object hologram with the point spread hologram. However, the image reconstructed by the conventional correlation algorithm suffers from serious background noise, which leads to poor imaging quality. In this work, via an effective combination of the speckle correlation and neural network, we propose a high-quality reconstruction strategy based on physics-informed deep learning. Specifically, this method takes the autocorrelation of the speckle image as the input of the network, and switches from establishing a direct mapping between the object and the image into a mapping between the autocorrelations of the two. This method improves the interpretability of neural networks through prior physics knowledge, thereby remedying the data dependence and computational cost. In addition, once a final model is obtained, the image reconstruction can be completed by one camera exposure. Experimental results demonstrate that the background noise can be effectively suppressed, and the resolution of the reconstructed images can be enhanced by three times.

Automated Camera Exposure Control for Accuracy-Enhanced Stereo-Digital Image Correlation Measurement.

An automated camera exposure control method, which allows a two-camera stereo-digital image correlation (stereo-DIC) system to capture high-quality speckle image pairs, is presented for accuracy-enhanced stereo-DIC measurement. By using this method, the two synchronized cameras can automatically determine the optimal camera exposure and ideal average grayscale for capturing the optimal reference image pair in the reference state. Furthermore, high-quality deformed image pairs can be recorded during the test by adaptively adjusting the camera exposure in case of serious ambient light variations. Validation tests, including varying illumination tests and translation tests, were performed to verify the effectiveness and robustness of this method. Experimental results indicate that the proposed method overperforms the existing stereo-DIC technique with empirically determined fixed camera exposure time. The practicality of the proposed automated camera exposure control method was verified using real high-temperature experiments.

Model of dynamic speckle evolution for evaluating laser speckle contrast measurements of tissue dynamics.

We introduce a dynamic speckle model (DSM) to simulate the temporal evolution of fully developed speckle patterns arising from the interference of scattered light reemitted from dynamic tissue. Using this numerical tool, the performance of laser speckle contrast imaging (LSCI) or speckle contrast optical spectroscopy (SCOS) systems which quantify tissue dynamics using the spatial contrast of the speckle patterns with a certain camera exposure time is evaluated. We have investigated noise sources arising from the fundamental speckle statistics due to the finite sampling of the speckle patterns as well as those induced by experimental measurement conditions including shot noise, camera dark and read noise, and calibrated the parameters of an analytical noise model initially developed in the fundamental or shot noise regime that quantifies the performance of SCOS systems using the number of independent observables (NIO). Our analysis is particularly focused on the low photon flux regime relevant for human brain measurements, where the impact of shot noise and camera read noise can become significant. Our numerical model is also validated experimentally using a novel fiber based SCOS (fb-SCOS) system for a dynamic sample. We have found that the signal-to-noise ratio (SNR) of fb-SCOS measurements plateaus at a camera exposure time, which marks the regime where shot and fundamental noise dominates over camera read noise. For a fixed total measurement time, there exists an optimized camera exposure time if temporal averaging is utilized to improve SNR. For a certain camera exposure time, photon flux value, and camera noise properties, there exists an optimized speckle-to-pixel size ratio (s/p) at which SNR is maximized. Our work provides the design principles for any LSCI or SCOS systems given the detected photon flux and properties of the instruments, which will guide the experimental development of a high-quality, low-cost fb-SCOS system that monitors human brain blood flow and functions.

Exposure Trajectory Recovery From Motion Blur.

Motion blur in dynamic scenes is an important yet challenging research topic. Recently, deep learning methods have achieved impressive performance for dynamic scene deblurring. However, the motion information contained in a blurry image has yet to be fully explored and accurately formulated because: (i) the ground truth of dynamic motion is difficult to obtain; (ii) the temporal ordering is destroyed during the exposure; and (iii) the motion estimation from a blurry image is highly ill-posed. By revisiting the principle of camera exposure, motion blur can be described by the relative motions of sharp content with respect to each exposed position. In this paper, we define exposure trajectories, which represent the motion information contained in a blurry image and explain the causes of motion blur. A novel motion offset estimation framework is proposed to model pixel-wise displacements of the latent sharp image at multiple timepoints. Under mild constraints, our method can recover dense, (non-)linear exposure trajectories, which significantly reduce temporal disorder and ill-posed problems. Finally, experiments demonstrate that the recovered exposure trajectories not only capture accurate and interpretable motion information from a blurry image, but also benefit motion-aware image deblurring and warping-based video extraction tasks. Codes are available on https://github.com/yjzhang96/Motion-ETR.

Framework to Georeference Point Cloud in GPS Shadowed Urban Environment by Integrating Computer Vision Algorithms and Photogrammetric Techniques

The integration of computer vision algorithms and photogrammetric techniques has become an alternative to the high-cost Mobile Mapping Systems (MMS) and point cloud generation through Structure from Motion (SfM) algorithm is the best example of it. The point cloud generated using SfM is an arbitrary coordinate system and for its georeferencing known global coordinates of the camera exposure stations, rotational and translational parameters are required. The global coordinates of exposure stations are obtained through GNSS (Global Navigation Satellite System). GPS (Global Positioning System) is widely used for getting the positional information of a point. The problem in georeferencing the point cloud arises if the coordinates of a few camera exposure stations are unknown due to GPS shadowing or poor GDOP (Geometric Dilution of Precision). This issue is common in MMS that use laser scanners, GNSS and IMU (inertial measurement unit). In this paper, efforts are made to develop a methodology for handling GPS shadowing or poor accuracy for the georeferencing of arbitrary point clouds generated through SfM. The adopted method uses a blend of photogrammetric techniques of space resection and space intersection to determine the unknown camera exposure stations' coordinates. Bundle adjustment is applied to improve the accuracy of the results obtained. The developed methodology is well analyzed in different cases, and the results show good accuracy in respective cases.

Multi-Thread AI Cameras Using High-Speed Active Vision System

In this study, we propose a multi-thread artificial intelligence (AI) camera system that can simultaneously recognize remote objects in desired multiple areas of interest (AOIs), which are distributed in a wide field of view (FOV) by using single image sensor. The proposed multi-thread AI camera consists of an ultrafast active vision system and a convolutional neural network (CNN)-based ultrafast object recognition system. The ultrafast active vision system can function as multiple virtual cameras with high spatial resolution by synchronizing exposure of a high-speed camera and movement of an ultrafast two-axis mirror device at hundreds of hertz, and the CNN-based ultrafast object recognition system simultaneously recognizes the acquired high-frame-rate images in real time. The desired AOIs for monitoring can be automatically determined after rapidly scanning pre-placed visual anchors in the wide FOV at hundreds of fps with object recognition. The effectiveness of the proposed multi-thread AI camera system was demonstrated by conducting several wide area monitoring experiments on quick response (QR) codes and persons in nature spacious scene such as meeting room, which was formerly too wide for a single still camera with wide angle lens to simultaneously acquire clear images.

PSXIII-B-6 Effect of Camera Exposure Time on Image Segmentation and Body Weight Prediction

Abstract External conditions such as lightness and brightness in a scene might have a tremendous effect on the quality of image acquisition, and this may influence on the prediction of phenotypes of interest. Here we investigated the impact of camera exposure time on the performance of image segmentation and body weight (BW) prediction. Top view depth images were acquired from 84 dairy cows using exposure time automatically defined by the camera (Exp0) given the scene light conditions, and manually set as 1,000 milliseconds (Exp1) and 4,000 milliseconds (Exp4) for all cows. Three images per cow from each experiment (n = 252) had the animal body manually traced. A 10-fold cross-validation (random by cow) strategy was used to train and validate three U-Net networks, one per experiment, for semantic segmentation. The trained segmenters were tested on each experiment dataset using intersection over union (IoU) as an evaluation metric. Biological features (BF) including area, volume, circularity, and major axis length were extracted from the segmented images. BF were used as predictors in linear models to predict the BW, and the models’ performance were evaluated using coefficient of determination (R2). IoU remained constant (0.98) when training U-Net using images from Exp0, Exp1, and Exp4 and testing on Exp0 and Exp1, and decreased (0.92) when testing on Exp4. R2 of 0.79, 0.78, and 0.08 were observed to predict BW when the model was trained using BF from Exp0 and Exp1 and tested using BF from Exp0, Exp1, and Exp4, respectively. Lower R2 (0.33, 0.34, and 0.25, respectively) were observed using BF from Exp4 to train the model and test using BF from Exp0, Exp1, and Exp4. Overall, higher exposure times (Exp4) resulted in poorer segmentation quality and consequently less precise BW predictions.

Assessment of angle-dependent spectral distortion to develop accurate hyperspectral endoscopy

Hyperspectral endoscopy has shown its potential to improve disease diagnosis in gastrointestinal tracts. Recent approaches in developing hyperspectral endoscopy are mainly focusing on enhancing image speed and quality of spectral information under a clinical environment, but there are many issues in obtaining consistent spectral information due to complicated imaging conditions, including imaging angle, non-uniform illumination, working distance, and low reflected signal. We quantitatively investigated the effect of imaging angle on the distortion of spectral information by exploiting a bifurcated fiber, spectrometer, and tissue-mimicking phantom. Spectral distortion becomes severe as increasing the angle of the imaging fiber or shortening camera exposure time for fast image acquisition. Moreover, spectral ranges from 450 to 550 nm are more susceptible to the angle-dependent spectral distortion than longer spectral ranges. Therefore, imaging angles close to normal and longer target spectral ranges with enough detector exposure time could minimize spectral distortion in hyperspectral endoscopy. These findings will help implement clinical HSI endoscopy for the robust and accurate measurement of spectral information from patients in vivo.

Automatic optimal camera exposure time control for digital image correlation

We present a method that can automatically determine the optimal camera exposure time for high-quality deformation measurement with digital image correlation (DIC) techniques. The proposed method needs to capture a series of surface images of a test sample at its reference state with different camera exposure times. The relationship between the mean intensity gradients (MIGs) and average grayscales of these images reveals that the best quality (i.e. maximum MIG) of a speckled sample surface always corresponds to a certain average grayscale. Thus, the proposed method can serve two purposes in DIC practice. First, at the initial state, the camera exposure time can be adjusted automatically to obtain a reference image with the best speckle pattern quality. Second, by adjusting the camera exposure time to make the average grayscale of an image close to the predetermined optimal value, the proposed method can adaptively output high-quality deformed images with an almost constant speckle pattern quality, regardless of serious ambient light variations. Experimental results demonstrated that the proposed method can automatically obtain stable and high-quality speckle pattern images, thus delivering better DIC measurement compared with regular DIC techniques using a fixed camera exposure time. Because the present automatic camera exposure time control method allows a nonprofessional operator to consistently obtain high-quality speckle pattern images that warrant high-accuracy DIC measurements, it is therefore suggested that the present method should be used as a routine practice in practical DIC applications.

Least-Square-Method-Based Optimal Laser Spots Acquisition and Position in Cooperative Target Measurement.

The relative positioning precisions of coordinate points is an important indicator that affects the final accuracy in the visual measurement system of space cooperative targets. Many factors, such as measurement methods, environmental conditions, data processing principles and equipment parameters, are supposed to influence the cooperative target’s acquisition and determine the precision of the cooperative target’s position in a ground simulation experiment with laser projected spots on parallel screens. To overcome the precision insufficiencies of cooperative target measurement, the factors of the laser diode supply current and charge couple device (CCD) camera exposure time are studied in this article. On the hypothesis of the optimal experimental conditions, the state equations under the image coordinates’ system that describe the laser spot position’s variation are established. The novel optimizing method is proposed by taking laser spot position as state variables, diode supply current and exposure time as controllable variables, calculating the optimal controllable variables through intersecting the focal spot centroid line and the 3-D surface, and avoiding the inconvenience of solving nonlinear equations. The experiment based on the new algorithm shows that the optimal solution can guarantee the focal spot’s variation range in 5–10 pixels under image coordinates’ system equivalent to the space with a 3 m distance and 0.6–1.2 mm positioning accuracy.

Direct characterization of tissue dynamics with laser speckle contrast imaging.

Laser speckle contrast imaging (LSCI) has gained broad appeal as a technique to monitor tissue dynamics (broadly defined to include blood flow dynamics), in part because of its remarkable simplicity. When laser light is backscattered from a tissue, it produces speckle patterns that vary in time. A measure of the speckle field decorrelation time provides information about the tissue dynamics. In conventional LSCI, this measure requires numerical fitting to a specific theoretical model for the field decorrelation. However, this model may not be known a priori, or it may vary over the image field of view. We describe a method to reconstruct the speckle field decorrelation time that is completely model free, provided that the measured speckle dynamics are ergodic. We also extend our approach to allow for the possibility of non-ergodic measurements caused by the presence of a background static speckle field. In both ergodic and non-ergodic cases, our approach accurately retrieves the correlation time without any recourse to numerical fitting and is largely independent of camera exposure time. We apply our method to tissue phantom and in-vivo mouse brain imaging. Our aim is to facilitate and add robustness to LSCI processing methods for potential clinical or pre-clinical applications.

A dual-camera single-shot lifetime method with fully controllable exposure gates for pressure-sensitive paint measurements on fast-rotating surfaces

This paper proposes a dual-camera single-shot lifetime method for pressure-sensitive paint (PSP) measurements on fast-rotating surfaces. Two cameras with fully controllable exposure gates are used for image acquisition to reduce the motion blur due to the open-ended exposure of the interline transfer charge-coupled device camera which was typically used for the single-shot lifetime method. The images can thus be processed without deblurring for a wider rotating speed range than is possible with existing techniques. More importantly, both frames have equal exposure times and therefore equal blur lengths. This enables precise pixel-to-pixel image matching and minimizes image registration errors. In addition, the signal strengths of the frames can be balanced, enabling shot-noise-induced errors to be reduced by independent adjustment of the aperture ratio. A pressure uncertainty estimation model is established to evaluate the measurement errors, which helps determine the system parameters. The dual-camera PSP system is calibrated and then validated through the surface pressure measurement of a fast-rotating disk. It is found that the exposure time t e has little effect on the PSP sensitivity, whereas the delay time t d affects both the pressure sensitivity and the measurement error. Both t e and t d are set to 5 μs in the rotating disk experiment. Images with a blur length of 1.4 pixels are captured and processed without deblurring to yield reasonable surface pressure results. The pressure error is decreased from 1.05 to 0.65 kPa by increasing the aperture ratio F from 2 to 4.

Учет физических факторов при проектировании топографической аэрофотосъемки

Despite the variety of flight task preparation programs represented in the market and introduction of new standards of technical requirements in topographic aerial photography, the design issues of the latter are not worked out in detail; the criteria of pixel size selection on the ground are not defined. The necessity of considering all the input data i.e. customer requirements to the outcome product, characteristics of the used technical means of flight support, navigation and photographic equipment, the impact of external factors of the photographic environment was noted in the paper. Within the framework of the unified methodical approach to the choice of the pixel size in the design, the use of theoretical and physical similarity criteria is proposed. The method provides analyzing aerial photography’s all external and internal factors complex interaction and consideration of their affecting the result. Physical criteria, i.e. stability of flight, image quality of images, and depth of depicted high-altitude objects sharpness are considered in detail. The theoretical justification of determining the similarity coefficients is given. It analyzes the changes in coefficients depending on focal length, camera exposure parameters, etc. The method of taking into account physical factors and estimation of design accuracy is presented. The software implementation of the method is described. The practical example of multi-factor data analysis for selection of pixel size for designing large-scale aerial photography of built-up area is considered. The relevance of determining the coefficients directly in the course of aerial survey is shown.

Visual measurement of valve opening area with improved subpixel edge location

The opening area error of the gas valve is mainly caused by machining, assembling errors and transmission clearances. Accurate measurement of the opening area is important for the production quality inspection of gas valves. This paper proposes a novel solution with a specific measurement system. Due to the reflective effect of the valve surface, the valve opening’s saliency is enhanced by the combination of top lighting and camera exposure. Then, a partial image is used to extract the single closed edge of the valve opening. To obtain the precise edge position, a subpixel location based on the rotating area effect is applied. Finally, the area computation based on the minimum circumscribed circle and the maximum inner circle of the edge is provided. The experiment results prove the system reliability, and the general significances of the proposed algorithms.