Camera Noise Research Articles

Efficient Human Motion Detection with Adaptive Background for Vision-Based Security System

Motion detection is very important in video surveillance system especially for video compression, human detection and behaviour analysis. Various approaches have been used for detecting motion in a continuous video stream but for real-time video surveillance system, we need a motion detection that can provide accurate detection even in non-static background regardless of surroundings (outdoor or indoor), object speed and size, robust to camera noisy pixels or sudden change in light intensity. This is very important to ensure that the security of a monitored parameter or area is not compromised. In this paper, we propose a method for human motion detection which employs adaptive background subtraction, camera noise reduction and white pixel counts threshold for real-time video streams.

Observation of pulsating aurora signatures in cosmic noise absorption data

AbstractThis study investigates the contribution of energetic (E > 30 keV) particle precipitation during a pulsating aurora event over Kilpisjärvi (L = 6.2) on 26 February 2014. It is based on the comparison of auroral blue‐line emission (427.8 nm) data from an all‐sky camera and cosmic noise absorption (CNA) data obtained from a multibeam experiment of the Kilpisjärvi Atmospheric Imaging Receiver Array (KAIRA) riometer. The data sets are compared for three KAIRA beams close to magnetic zenith. Results show a clear correlation between the measured CNA and the auroral blue‐line emission during the event, for each beam. In addition, individual pulsations are observed for the first time in the cosmic noise absorption data measured by KAIRA and are found to be close‐to‐identical to the optical pulsations. This suggests that the modulation of electron precipitation during pulsating aurora takes place in a consistent way over a broad range of energies.

Suppressing Random Artifacts in Reference Sensor Pattern Noise via Decorrelation

Photo response nonuniformity noise is a component of sensor pattern noise (SPN) of digital cameras. It characterizes the imaging devices of cameras and has been widely used in source camera identification (SCI). However, the reference SPN always be contaminated by unwanted nonunique artifacts. Previous works reported that these nonunique artifacts were periodic signals shared among cameras, which undergo the same or similar in-camera processing procedures. In this letter, we first illustrate that these nonunique artifacts, in addition to periodic signals, also contain random signals caused by the imperfection of SPN extraction method. To improve the quality of SPN and the performance of SCI, then we propose a scheme to suppress these random signals by decorrelation. In our proposed method, the reference SPN is modeled as a white Gaussian noise, while the random signals introduced by SPN extraction method are correlated noises, we suppress these correlated components to purify the SPN and enhance the performance of SCI. The experimental results on “Dresden” database show that the proposed scheme achieves better receiver operating characteristic curves than the state-of-the-art methods.

Statistical characterization of evaluation strategies for fringe projection systems by means of a model-based noise prediction

Abstract. For optical 3-D measurement systems, camera noise is the dominant uncertainty factor when optically cooperative surfaces are measured in a stable and controlled environment. In industrial applications repeated measurements are seldom executed for this kind of measurement system. This leads to statistically suboptimal results in subsequent evaluation steps as the important information about the quality of individual measurement points is lost. In this work it will be shown that this information can be recovered for phase measuring optical systems with a model-based noise prediction. The capability of this approach will be demonstrated exemplarily for a fringe projection system and it will be shown that this method is indeed able to generate an individual estimate for the spatial stochastic deviations resulting from image sensor noise for each measurement point. This provides a valuable tool for a statistical characterization and comparison of different evaluation strategies, which is demonstrated exemplarily for two different triangulation procedures.

Monocular vision-based range estimation supported by proprioceptive motion

This paper describes an approach for fusion of monocular vision measurements, camera motion, odometer and inertial rate sensor measurements. The motion of the camera between successive images generates a baseline for range computations by triangulation. The recursive estimation algorithm is based on extended Kalman filtering. The depth estimation accuracy is strongly affected by the mutual observer and feature point geometry, measurement accuracy of observer motion parameters and line of sight to a feature point. The simulation study investigates how the estimation accuracy is affected by the following parameters: linear and angular velocity measurement errors, camera noise, and observer path. These results impose requirements to the instrumentation and observation scenarios. It was found that under favorable conditions the error in distance estimation does not exceed 2% of the distance to a feature point.

The Juno Radiation Monitoring (RM) Investigation

The Radiation Monitoring Investigation of the Juno Mission will actively retrieve and analyze the noise signatures from penetrating radiation in the images of Juno’s star cameras and science instruments at Jupiter. The investigation’s objective is to profile Jupiter’s \(>10\mbox{-MeV}\) electron environment in regions of the Jovian magnetosphere which today are still largely unexplored. This paper discusses the primary instruments on Juno which contribute to the investigation’s data suite, the measurements of camera noise from penetrating particles, spectral sensitivities and measurement ranges of the instruments, calibrations performed prior to Juno’s first science orbit, and how the measurements may be used to infer the external relativistic electron environment.

Super-resolved 3-D imaging of live cells’ organelles from bright-field photon transmission micrographs

Current biological and medical research is aimed at obtaining a detailed spatiotemporal map of a live cell’s interior to describe and predict cell’s physiological state. We present here an algorithm for complete 3-D modelling of cellular structures from a z-stack of images obtained using label-free wide-field bright-field light-transmitted microscopy. The method visualizes 3-D objects with a volume equivalent to the area of a camera pixel multiplied by the z-height. The computation is based on finding pixels of unchanged intensities between two consecutive images of an object spread function. These pixels represent strongly light-diffracting, light-absorbing, or light-emitting objects. To accomplish this, variables derived from Rényi entropy are used to suppress camera noise. Using this algorithm, the detection limit of objects is only limited by the technical specifications of the microscope setup–we achieve the detection of objects of the size of one camera pixel. This method allows us to obtain 3-D reconstructions of cells from bright-field microscopy images that are comparable in quality to those from electron microscopy images.

Indoor Scene Reconstruction Using Near-Light Photometric Stereo.

We propose a novel framework for photometric stereo (PS) under low-light conditions using uncalibrated near-light illumination. It operates on free-form video sequences captured with a minimalistic and affordable setup. We address issues such as albedo variations, shadowing, perspective projections, and camera noise. Our method uses specular spheres detected with a perspective-correcting Hough transform to robustly triangulate light positions in the presence of outliers via a least-squares approach. Furthermore, we propose an iterative reweighting scheme in combination with an ℓp-norm minimizer to robustly solve the calibrated near-light PS problem. In contrast to other approaches, our framework reconstructs depth, albedo (relative to light source intensity), and normals simultaneously and is demonstrated on synthetic and real-world scenes.

Low-Rankness Transfer for Realistic Denoising

Current state-of-the-art denoising methods, such as non-local low-rank approaches, have shown to give impressive results. They are, however, mainly tuned to work with uniform Gaussian noise corruption and known variance, which is far from the real noise scenario. In fact, noise level estimation is already a challenging problem and denoising methods are quite sensitive to this parameter. Moreover, these methods are based on shrinkage models that are too simple to reflect reality, which results in over-smoothing of important structures, such as small-scale text and textures. We propose in this paper a new approach for more realistic image restoration based on the concept of low-rankness transfer. Given a training clean/noisy image pair, our method learns a mapping between the non-local noisy singular values and the optimal values for denoising to be transfered to a new noisy input. One single image is enough for training the model and can be adapted to the noisy input by taking a correlated image. Experiments conducted on synthetic and real camera noise show that the proposed method leads to an important improvement both visually and in terms of PSNR/SSIM.

Video recording true single-photon double-slit interference

Commercially available cameras do not have a low-enough dark noise to directly capture double-slit interference at the single photon level. In this work, camera noise levels are significantly reduced by activating the camera only when the presence of a photon has been detected by the independent detection of a time-correlated photon produced via parametric down-conversion. This triggering scheme provides the improvement required for direct video imaging of Young's double-slit experiment with single photons, allowing clarified versions of this foundational demonstration. We present video data of the evolving interference patterns. Also, we introduce variations on this experiment aimed at promoting discussion of the role spatial coherence plays in such a measurement, emphasizing complementary aspects of single-photon measurement and highlighting the roles of transverse position and momentum correlations between down-converted photons, including examples of “ghost” imaging and diffraction.

Directional coherence-based spatiotemporal descriptor for object detection in static and dynamic scenes

This paper presents a simple, yet powerful local descriptor, so-called the histograms of space---time dominant orientations (HiSTDO). Specifically, our HiSTDO is composed of two main components, i.e., the dominant orientation and its coherence, which represents how intensively gradients in the local region are distributed along the space---time dominant orientation. By incorporating them into the histogram, we define it as our HiSTDO descriptor. In contrast to previous methods vulnerable to the presence of the background clutter and the camera noise, our HiSTDO greatly encodes the space---time shape of underlying structures even under such challenging conditions, and it can thus be efficiently applied to various applications (e.g., object and action detection). Experimental results on diverse datasets demonstrate that the proposed descriptor is effective for human action as well as object detection.

Novel Real-Time Tone-Mapping Operator for Noisy Logarithmic CMOS Image Sensors

Logarithmic CMOS image sensors are easily able, at video rates, to capture scenes where the dynamic range (DR) is high. However, tone mapping is required to output resulting images or videos to standard low-DR displays. This article proposes a new method, designed especially for logarithmic CMOS image sensors, which can suffer from temporal, and residual fixed pattern, noise. The novel tone mapping, a global operator based on histogram adjustment, uses a model of the camera noise to ensure that the mapping does not amplify the noise above a display threshold. Moreover, to reduce the likelihood of flickering, a temporal adaptation process is incorporated into the histogram calculation. Furthermore, to reduce complexity for real-time processing, a fixed-point implementation is designed for the proposed tone mapping. The novel operator and its fixed-point design are validated through offline and real-time experiments with a logarithmic CMOS image sensor.

Novel Real-Time Tone-Mapping Operator for Noisy Logarithmic CMOS Image Sensors

Logarithmic CMOS image sensors are easily able, at video rates, to capture scenes where the dynamic range (DR) is high. However, tone mapping is required to output resulting images or videos to standard low-DR displays. This article proposes a new method, designed especially for logarithmic CMOS image sensors, which can suffer from temporal, and residual fixed pattern, noise. The novel tone mapping, a global operator based on histogram adjustment, uses a model of the camera noise to ensure that the mapping does not amplify the noise above a display threshold. Moreover, to reduce the likelihood of flickering, a temporal adaptation process is incorporated into the histogram calculation. Furthermore, to reduce complexity for real-time processing, a fixed-point implementation is designed for the proposed tone mapping. The novel operator and its fixed-point design are validated through offline and real-time experiments with a logarithmic CMOS image sensor. © 2016 Society for Imaging Science and Technology. [DOI: 10.2352/J.ImagingSci.Technol.2016.60.2.020404]

Autonomous Onboard Point Source Detection by Small Exploration Spacecraft

Small spacecraft platforms are a promising low-cost approach to accelerate exploration of small bodies, addressing the space community’s interest in origin science, planetary resources, and planetary defense. However, they can be challenging platforms for detecting and imaging low brightness targets. Difficulties include constrained bandwidth, which limits the volume of data that can be downlinked; attitude instability, which limits exposure time; small instrument apertures, which reduce sensitivity; and cosmic ray contamination, which creates illusory sources. Mission designers can address all these problems simultaneously by shifting image analysis across the communications gap. Spacecraft can use onboard data analysis to detect sources directly, or downlink parsimonious summary products for detection on the ground. One promising approach is to acquire stacks of short consecutive exposures, and then coregister and coadd them onboard. This work analyzes a coaddition algorithm that is designed to be robust against small spacecraft challenges. We evaluate factors affecting performance, such as attitude control and camera noise systematics, in regimes typical of small spacecraft missions. We motivate the algorithm design by considering its application to NEAScout, a mission representing a new generation of small (sub-50 kg) exploration spacecraft having very small instrument apertures and data rates below 1 kbyte s-1. Here, onboard analysis allows detection and rendezvous with far smaller and fainter objects, dramatically reducing the cost and complexity of primitive bodies exploration.

Using local binary patterns for object detection in images

The article discusses a texture operator called Local Binary Patterns (LBP) and its applications in image processing and object detection. We provide a description of the algorithm for computing LBP together with a rationale for using LBP as a feature for object detection and image recognition. Based on the algorithm we show that LBP features have a low computational overhead compared to more complicated image features such as the commonly used SIFT or SURF features or neural network based approaches because they exploit the use of extremely fast bitwise and integer operations of the CPU. We demonstrate that LBP is robust to changes in brightness, contrast, image rotation, image scale. We develop two enhancements for LBP that improve its resistance to camera noise and enhance the discriminative power of LBP when it is used as a feature for machine learning algorithms. We present the results on a challenging real-world object detection task. Keywords: computer vision, object detection, local binary patterns.

Period correction method of phase coding fringe

Phase is better immune to surface contrast variations, ambient light, and camera noises, compared with intensity. A method that combines phase coding fringe with sinusoidal fringe is used to achieve 3D shape measurement of complex surface. However, because of the projection angle deviation, the captured fringe period lacks consistency and the measurement error is induced. Evident error is caused when larger object is measured. Period correction method of phase coding fringe is proposed. The corrected fringe patterns are projected by digital light processing, and then the desired uniform fringe patterns are captured by the CCD camera. Experiment results demonstrate that the proposed method can solve the broadening of fringes and improve the measurement accuracy. This method can be widely employed for measuring larger object with different contrast surfaces.

Interpretation of angular symmetries in electron nanodiffraction patterns from thin amorphous specimens.

The interpretation of angular symmetries in electron nanodiffraction patterns from thin amorphous specimens is examined. It is found that in general there are odd symmetries in experimental electron nanodiffraction patterns. Using simulation, it is demonstrated that this effect can be attributed to dynamical scattering, rather than other divergences from the ideal experimental conditions such as probe-forming lens aberrations and camera noise. The departure of opposing diffracted intensities from Friedel's law in the phase grating formalism is calculated using a general structure factor for disordered materials. On the basis of this, a simple correction procedure is suggested to recover the kinematical angular symmetries, and thus readily interpretable information that reflects the symmetries of the original projected object. This correction is numerically tested using both the phase object and multislice calculations, and is demonstrated to fully recover all the kinematical diffracted symmetries from a simulated atomic model of a metallic glass.

Measuring random sensor noise in cameras

Measuring random sensor noise in cameras

Video-Rate Super Resolution Microscopy in Living Cells

Super resolution microscopy based on single-molecule localization relies on precise and accurate localization of large numbers of single-molecules. However, the necessity of accumulating large numbers of localization estimates limits the time resolution typically to seconds to minutes1,2.Two major limitations are the acquisition speed and the photon budget. Replacing the usually used EMCCD with a recently introduced sCMOS camera results in leaps in both acquisition speed and effective quantum efficiency. However, the intrinsic pixel-dependent Gaussian noise of the sCMOS cameras introduces localization artifacts and greatly reduces the reliability of the results.Here, we present a set of specially designed methods that characterize an sCMOS camera for the first time and allow for unbiased and precise localization analysis. Using this method we demonstrate Cramer-Rao lower bound-limited single-molecule localization with an sCMOS camera. Combining the novel algorithm with a recently developed multi-emitter fitting algorithm3, We shortens the typical acquisition time for fixed samples by up to two orders of magnitude without compromising the field of view. Furthermore, we demonstrate localization-based super-resolution microscopy in live cells by monitoring dynamics of protein clusters, vesicles and organelles at a temporal resolution from 2 to 30 frames per second4.These methods allowed us to investigate cytokinetic apparatus in live fission yeast at 20-30 nm resolution. In general, the significantly improved temporal resolution allows super resolution imaging of a large range of dynamic events in living cells.1. Patterson, G., Davidson, M., Manley, S. & Lippincott-Schwartz, J. Annu. Rev. Phys. Chem.61, 345-67 (2010).2. Gould, T. J., Hess, S. T. & Bewersdorf, J. Annu. Rev. Biomed. Eng.14, 231-54 (2012).3. Huang, F., Schwartz, S. L., Byars, J. M. & Lidke, K. A. Biomed. Opt. Express 2, 1377-93 (2011).4. Huang, F. et al.Nat. Methods 10, 653-8 (2013).

Measuring the Noise of Digital Imaging Sensors by Stacking Raw Images Affected by Vibrations and Illumination Flickering

This paper discusses camera noise estimation from a series of raw images of an arbitrary natural static scene, acquired with the same camera settings. Although it seems natural to characterize noise from the random time fluctuation of pixel intensity, it turns out that these fluctuations may also be caused by illumination flickering and mechanical microvibrations affecting the camera. In this context, the contributions are twofold. First, a theoretical model of image formation in the presence of illumination flickering and of vibrations is discussed. This parametric model is based on a Cox process. It is shown that illumination flickering changes the standard affine relation between noise variance and average intensity to a quadratic relation. Second, under these conditions an algorithm is proposed to estimate the main parameters governing sensor noise, namely the gain, the offset, and the readout noise. The rolling shutter effect, which potentially affects the output of any focal-plane shutter camera, is...