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Acoustic measurements or ultrasonic testing can be strongly affected by reflections or echoes from test tank walls. In order to create a non-reflecting environment equivalent to infinite medium, a pyramidal structured absorber (PSA) can be used to coat the walls of an ultrasonic tank. In this work, we model an array of tetragonal pyramid ultrasonic wave absorbers. This model is based on two coupled first-order equations describing the stress and particle velocity within an isotropic medium. For absorbing media, the Kelvin-Voigt model of viscoelasticity is used. The equations are discretized in 2D using an efficient time-stepping pseudo-spectral scheme that takes in consideration both, the acoustic properties and attenuation characteristics of the composite materials. We then built a 3D printed PSA using a Stratasys Objet500 Connex 3D printer, which allows to combine photopolymers in specific concentrations and microstructures. We designed PSA covering the frequency ranges from 0.5 MHz to 5 MHz and from 1 MHz to 10 MHz, with double homogeneous layer: a core of rubber material with a skin of a variety of elastomers by combining rigid and flexible materials. Each single pyramid contains two major parts: the ground of the pyramid (9.4 mm base × 4.7 mm height, for 0.5 MHz and 4.7 mm base × 2.35 mm height, for 1 MHz) and the body of the pyramid (23.5 mm height, for 0.5 MHZ and 11.75 mm height, for 1 MHz). The measured echo-reduction was greater than 35 dB at the covering frequency range and the transmission loss was estimated by 20 dB. Echoes increase rapidly for frequencies below the minimum frequency of the covering range. The modeling and 3D printing of PSA with different sizes, in a wide range of frequencies, is a cost-effective custom solution for a wide range of applications including for example, radiation force balances, hydrophone mounts and medical ultrasound equipment.

The use of axicon lenses is useful in many high-resolution-focused ultrasound applications, such as mapping, detection, and have recently been extended to ultrasonic brain therapies. However, in order to achieve high spatial resolution with an axicon lens, it is necessary to adjust the separation, called stand-off (&delta, ), between a conventional transducer and the lens attached to it. Comprehensive ultrasound simulations, using the open-source k-Wave toolbox, were performed for an axicon lens attached to a piezo-disc type transducer with a radius of 14 mm, and a frequency of about 0.5 MHz, that is within the range of optimal frequencies for transcranial transmission. The materials properties were measured, and the lens geometry was modelled. Hydrophone measurements were performed through a human skull phantom. We obtained an initial easygoing design model for the lens angle and optimal stand-off using relatively simple formulas. The skull is not an obstacle for focusing of ultrasound with optimized axicon lenses that achieve an identical resolution to spherical transducers, but with the advantage that the focusing distance is shortened. An adequate stand-off improves the lateral resolution of the acoustic beam by approximately 50%. The approach proposed provides an effective way of designing polydimethylsiloxane (PDMS)-based axicon lenses equipped transducers.

We propose a 3D full-field focusing method for microscopic mid-wave infrared (MWIR) imagery. The method is based on the experimental estimation of a confined volumetric vision microscope point spread function. The technique employs our well-known constant-range-based nonuniformity correction algorithm as a preprocessing step and then an iteration in the z -axis Fourier-based deconvolution. The technique’s ability to compensate for localized blur is demonstrated using two different real MWIR microscopic video sequences, captured from two microscopic living organisms using a Janos-Sofradir MWIR microscopy setup. The performance of the proposed algorithm is assessed on real and simulated noisy infrared data by computing the root-mean-square error and the roughness Laplacian pattern indexes, which are specifically developed for the present work.

In this work, mid wavelength infrared microscopy imaging videos of several index finger pads, from voluntary people, are recorded to obtain their thermoregulation curves. The proposed non-invasive technique is able to capture spatial and temporal thermal information emitted from blood vessels under-skin, and the irrigation finger pad system, making possible to capture features that a visual-spectrum microscopy cannot detect. Using an infrared laboratory prepared method several voluntary patients exposed theirs fingers to thermal stress while the infrared data is recorded. Using standard infrared imaging and signal processing techniques the thermoregulation curves are estimated. The Cold/Hot Stress experiments have shown infrared data with exponential trend curves, with different recovering slopes for each voluntary person, and sometimes with two steps increasing slope in one person thermoregulation curve response.

In this paper, a new empirical model for the spatial structure of the fixed-pattern noise (FPN) observed in infrared (IR) focal-plane arrays (FPA) is presented. The model was conceived after analyzing, in the spatial frequency domain, FPN calibration data from different IR cameras and technologies. The analysis showed that the spatial patterns of the FPN are retained in the phase spectrum, while the noise intensity is determined by the magnitude spectrum. Thus, unlike traditional representations, the proposed model abstracts the FPN structure using one matrix for its magnitude spectrum and another matrix for its phase spectrum. Three applications of the model are addressed here. First, an algorithm is provided for generating random samples of the FPN with the same spatial pattern of the actual FPN. Second, the model is used to assess the performance of non-uniformity correction (NUC) algorithms in the presence of spatially correlated and uncorrelated FPN. Third, the model is used to improve the NUC capability of a method that requires, as a reference, a proper FPN sample.

A novel and custom-made midwave infrared microscope was utilized to obtain thermal maps of index finger pads of different human subjects. From the acquired images we were able to recognize typical anatomical structures and we identified three thermal patterns amongst all the volunteers.

In this article, we present a novel algorithm to achieve simultaneous digital super-resolution and nonuniformity correction from a sequence of infrared images. We propose to use spatial regularization terms that exploit nonlocal means and the absence of spatial correlation between the scene and the nonuniformity noise sources. We derive an iterative optimization algorithm based on a gradient descent minimization strategy. Results from infrared image sequences corrupted with simulated and real fixed-pattern noise show a competitive performance compared with state-of-the-art methods. A qualitative analysis on the experimental results obtained with images from a variety of infrared cameras indicates that the proposed method provides super-resolution images with significantly less fixed-pattern noise.

The statistics of photon coincidence counting in photon-correlated beams is thoroughly investigated considering the effect of the finite coincidence resolving time. The correlated beams are assumed to be generated using parametric downconversion, and the photon streams in the correlated beams are modeled by two partially correlated Poisson point processes. An exact expression for the mean rate of coincidence registration is developed using techniques from renewal theory. It is shown that the use of the traditional approximate rate, in certain situations, leads to the overestimation of the actual rate. The error between the exact and approximate coincidence rates increases as the coincidence-noise parameter, defined as the mean number of uncorrelated photons detected per coincidence resolving time, increases. The use of the exact statistics of the coincidence becomes crucial when the background noise is high or in cases when high precision measurement of coincidence is required. Such cases arise whenever the coincidence-noise parameter is even slightly in excess of zero. It is also shown that the probability distribution function of the time between consecutive coincidence registration can be well approximated by an exponential distribution function. The well-known and experimentally verified Poissonian model of the coincidence registration process is therefore theoretically justified. The theory is applied to an on-off keying communication system proposed by Mandel which has been shown to perform well in extremely noisy conditions. It is shown that the bit-error rate (BER) predicted by the approximate coincidence-rate theory can be significantly lower than the actual BER obtained using the exact theory.

A channel-capacity metric is introduced for assessing the performance of single-photon avalanche photodiodes (SPADs) when used as detectors in laser communication systems. This metric is employed to theoretically optimize, with respect to the device structure and operating voltage, the performance of SPADs with simple InP or In/sub 0.52/Al/sub 0.48/As-InP heterojunction multiplication regions. As the multiplication-region width increases, an increase is predicted in both the peak and the full-width at half-maximum of the channel capacity curve versus the normalized excess voltage. Calculations also show the existence of an optimal In/sub 0.52/Al/sub 0.48/As-InP heterojunction multiplication region that maximizes the peak channel capacity beyond that of InP.

The clam Mulinia edulis is occasionally infected by the parasite Edotea magellanica. In this paper the normalized optical transmittance of the clam and its parasite have been disclosed for the first time using a hyperspectral imaging system. It has been identified that, in the spectral band of 600–950 nm, the normalized optical transmittance of clam’s mantle cavity changes in the presence or absence of the parasite. This relative change in the normalized optical transmittance has been used as an effective spectral feature for designing parasite detectors. As a proof of concept two detectors have been designed. The first detector, which relies on all the hyperspectral information, achieved a perfect detection accuracy in identifying parasite-infected clams. The second detector, which is based on a reduced number of hyperspectral bands, achieved an 85% detection accuracy. These results provide insights on the fundamental trade-off between detection accuracy and the amount of hyperspectral information for parasite detection in clams.

In this paper, a novel automated algorithm to estimate and remove the continuous baseline from measured flame spectra is proposed. The algorithm estimates the continuous background based on previous information obtained from a learning database of continuous flame spectra. Then, the discontinuous flame emission is calculated by subtracting the estimated continuous baseline from the measured spectrum. The key issue subtending the learning database is that the continuous flame emissions are predominant in the sooty regions, in absence of discontinuous radiation. The proposed algorithm was tested using natural gas and bio-oil flames spectra at different combustion conditions, and the goodness-of-fit coefficient (GFC) quality metric was used to quantify the performance in the estimation process. Additionally, the commonly used first derivative method (FDM) for baseline removing was applied to the same testing spectra in order to compare and to evaluate the proposed technique. The achieved results show that the proposed method is a very attractive tool for designing advanced combustion monitoring strategies of discontinuous emissions.

The aim of this research is to experimentally validate a Gauss-Markov model, previously developed by our group, for the non-uniformity parameters of infrared (IR) focal plane arrays (FPAs). The Gauss-Markov model assumed that both, the gain and the offset parameters at each detector, are random state-variables modeled by a recursive discrete-time process. For simplicity, however, we have regarded here the gain parameter as a constant and assumed that solely the offset parameter follows a Gauss-Markov model. Experiments have been conducted at room temperature and IR data was collected from black-body radiator sources using microbolometer-based IR cameras operating in the 8 to 12 μm. Next, well-known statistical techniques were used to analyze the offset time series and determinate whether the Gauss-Markov model truly fits the temporal dynamics of the offset. The validity of the Gauss-Markov model for the offset parameter was tested at two time scales: seconds and minutes. It is worth mentioning that the statistical analysis conducted in this work is a key in providing mechanisms for capturing the drift in the fixed pattern noise parameters.

In this paper the effects of the internal temperature on the response of uncooled microbolometer cameras have been studied. To this end, different temperature profiles steering the internal temperature of the cameras have been generated, and black-body radiator sources have been employed as time and temperature constant radiation inputs. The analysis conducted over the empirical data has shown the existence of statistical correlation between camera's internal temperature and the fluctuations in the read-out data. Thus, when measurements of the internal temperature are available, effective methods for compensating the fluctuations in the read-out data can be developed. This claim has been tested by developing a signal processing scheme, based on a polynomial model, to compensate for the output of infrared cameras equipped with amorphous-Silicon and Vanadium-Oxide microbolometers.

Our group has developed a Planck physics-based model for the input/output behavior of near infrared (NIR) hyperspectral cameras. During the validation of the model, experiments conducted using an NIR hyperspectral camera have shown that, when thermal radiation is used as the camera input and no illumination is present, the output offset happens to be thermally dependent, yet independent of the wavelengths in the NIR band. In this work, the effect of the incident temperature on the amount of output offset in NIR hyperspectral cameras has been experimentally studied and introduced in our previous model for such cameras. The experimental study has been conducted using an NIR hyperspectral camera in the range of 900 to 1700 [nm] and a controlled illumination set-up, while different input temperatures have been controlled by means of black-body radiator sources. The thermal-dependent offset is modeled phenomenologically from experimental data. Initial results have shown a non-linear dependence between the offset and the temperature. This thermal-offset dependence can be used to generate new NIR hyperspectral models, new non-linear calibration procedures, and establish a basis for the study of time dependent variations of the NIR thermal-offset.

The accuracy achieved by applications employing hyperspectral data collected by hyperspectral cameras depends heavily on a proper estimation of the true spectral signal. Beyond question, a proper knowledge about the sensor response is key in this process. It is argued here that the common first order representation for hyperspectral NIR sensors does not represent accurately their thermal wavelength-dependent response, hence calling for more sophisticated and precise models. In this work, a wavelength-dependent, nonlinear model for a near infrared (NIR) hyperspectral camera is proposed based on its experimental characterization. Experiments have shown that when temperature is used as the input signal, the camera response is almost linear at low wavelengths, while as the wavelength increases the response becomes exponential. This wavelength-dependent behavior is attributed to the nonlinear responsivity of the sensors in the NIR spectrum. As a result, the proposed model considers different nonlinear input/output responses, at different wavelengths. To complete the representation, both the nonuniform response of neighboring detectors in the camera and the time varying behavior of the input temperature have also been modeled. The experimental characterization and the proposed model assessment have been conducted using a NIR hyperspectral camera in the range of 900 to 1700 [nm] and a black body radiator source. The proposed model was utilized to successfully compensate for both: (i) the nonuniformity noise inherent to the NIR camera, and (ii) the stripping noise induced by the nonuniformity and the scanning process of the camera while rendering hyperspectral images.

The multiplicative and additive components of the fixed-pattern noise (FPN) in infrared (IR) focal plane arrays (FPAs) are typically modeled as time-stationary, spatially unstructured random processes. Even though the latter assumption is convenient, it is also inaccurate due to FPN is indeed observed as a spatial pattern, with random intensity values, superimposed over the true images. In this paper, the spatial structure in both the multiplicative and the additive components of the FPN has been modeled in the frequency domain. The key observation in the proposed models is that regular spatial patterns manifest themselves as narrowband components in the magnitude spectrum of an image. Thus, the spatial structure of FPN can be abstracted in a straightforward manner by approximating the spectral response of the FPN. Moreover, the random intensity of the FPN has been also modeled by matching the empirically estimated distributions of the intensity values of both multiplicative and additive components of the FPN. Experimental characterization of FPN has been conducted using black-body radiator sources, and the theoretical as well as practical applicability of the proposed models has been illustrated by both synthesizing FPN from three different IR cameras and by proposing a simple yet effective metric to assess the amount of FPN in FPA-based cameras.

In this Letter we propose an adaptive scene-based nonuniformity correction method for fixed-pattern noise removal in imaging arrays. It is based on the minimization of the total variation of the estimated irradiance, and the resulting function is optimized by an isotropic total variation approach making use of an alternating minimization strategy. The proposed method provides enhanced results when applied to a diverse set of real IR imagery, accurately estimating the nonunifomity parameters of each detector in the focal-plane array at a fast convergence rate, while also forming fewer ghosting artifacts.

Infrared (IR) Focal plane array (IRFPA) cameras are nowadays both, more accessible and with a broad variety in terms of detectors design. In many cases, the IRFPA characterization is not completely given by the manufacturer. In this paper a long wave 8-12 [μm] microbolometer IRFPA is characterized by means of calculating the Noise Equivalent Temperature Difference (NETD) and the Correctability performance parameters. The Correctability parameter has been evaluated by using a black body radiator and Two-Points calibration technique. Also, the Transfer Function of the microbolometer IR camera has been experimentally obtained as well as the NETD by the evaluation of radiometric data from a blackbody radiator. The obtained parameters are the key for any successful application of IR imaging pattern recognition.

We have developed a recursive estimator for the offset nonuniformity (NU) present in infrared (IR) focal-plane-array (FPA) imaging systems. The estimator is optimal in the least-square error sense and estimates the offset NU using only the collected scene data. Our scene-based nonuniformity correction (NUC) method exploits Kalman’s theory in order to estimate a constant in noise. The method is based upon two key assumptions: (i) the input irradiance at each detector is assumed to be a random variable uniformly distributed in a range that is common to all detectors in the IR–FPA; and (ii) the IR data sequences considered are temporally short enough such that the offset NU at each pixel of the array remains constant within the sequence. The proposed algorithm iterates over the frames in a pixel-by-pixel basis and updates two filter parameters for each pixel over the IR–FPA. Additionally, an on-line technique for ghosting artifacts cancellation is introduced to the NUC filter and an index to assess the artifact reduction is developed. The ability of the method to compensate for the offset NU is demonstrated by filtering video sequences from mid- and long-wave IR cameras.

In this paper, the face recognitions rate performance using infrared imagery is improved by adding nonuniformity pre-processing techniques. The infrared spectra contains the heat energy emitted by a face and it naturally present an insensitive behavior to variations in illuminations. Infrared imaging system can be formed by a Focal-Plane-Array technology, a group of photodetectors located in the focal plane of an imaging systems, but inherently present the nonuniformity as fixed-pattern noise that degrades the quality of infrared images. Additionally, this nonuniformity slowly varies over time, and depending on the technology used, this drift can take from minutes to hours. Due to this, the face identification performance is degraded over time, requiring a continuous-time calibrations method in order to maintain the face recognition rate using infrared imaging system. In synthesis, this work focuses on the evaluation of the degradation in pattern recognition performance produced by the fixed-pattern noise and the improvement when nonuniformity correction techniques is applied.

In this paper a novel nonuniformity correction method that compensates for the fixed-pattern noise (FPN) in infrared focal-plane array (IRFPA) sensors is developed. The proposed NUC method compensates for the additive component of the FPN statistically processing the read-out signal using a noise-cancellation system. The main assumption of the method is that a source of noise correlated to the additive noise of the IRFPA is available to the system. Under this assumption, a finite impulse response (FIR) filter is designed to synthesize an estimate of the additive noise. Moreover, exploiting the fact that the assumed source of noise is constant in time, we derive a simple expression to calculate the estimate of the additive noise. Finally, the estimate is subtracted to the raw IR imagery to obtain the corrected version of the images. The performance of the proposed system and its ability to compensate for the FPN are tested with infrared images corrupted by both real and simulated nonuniformity.

A noncontact infrared (IR) imaging-based methodology and signal recovery tools are applied on an enzyme reaction as a test target. The method is implemented by a long-wave (8-12 μm) IR microbolometer imaging array and a germanium-based IR optical vision. The reaction is carried out by the glucokinase, which produces a rapid exothermal release of energy that is weak, and, even worse, the IR video captured by the uncooled microbolometer detector is affected by spatial and temporal noise with specific complexities. Hitherto, IR-based signal recovery tools have worked with a standard acquisition frequency, which is clearly beyond the time scale of a real scenario. The implications of this (and similar) rapid reactions motivate the designs of a signal recovery method using prior information of the processes to extract and quantify the spontaneity of the enzymatic reaction in a three-dimensional (space and time) single and noncontact online measurement.

We present an adaptive technique for the estimation of nonuniformity parameters of infrared focal-plane arrays that is robust with respect to changes and uncertainties in scene and sensor characteristics. The proposed algorithm is based on using a bank of Kalman filters in parallel. Each filter independently estimates state variables comprising the gain and the bias matrices of the sensor, according to its own dynamic-model parameters. The supervising component of the algorithm then generates the final estimates of the state variables by forming a weighted superposition of all the estimates rendered by each Kalman filter. The weights are computed and updated iteratively, according to the a posteriori-likelihood principle. The performance of the estimator and its ability to compensate for fixed-pattern noise is tested using both simulated and real data obtained from two cameras operating in the mid- and long-wave infrared regime.

What is to our knowledge a new scene-based algorithm for nonuniformity correction in infrared focal-plane array sensors has been developed. The technique is based on the inverse covariance form of the Kalman filter (KF), which has been reported previously and used in estimating the gain and bias of each detector in the array from scene data. The gain and the bias of each detector in the focal-plane array are assumed constant within a given sequence of frames, corresponding to a certain time and operational conditions, but they are allowed to randomly drift from one sequence to another following a discrete-time Gauss-Markov process. The inverse covariance form filter estimates the gain and the bias of each detector in the focal-plane array and optimally updates them as they drift in time. The estimation is performed with considerably higher computational efficiency than the equivalent KF. The ability of the algorithm in compensating for fixed-pattern noise in infrared imagery and in reducing the computational complexity is demonstrated by use of both simulated and real data.

A novel statistical approach is undertaken for the adaptive estimation of the gain and bias nonuniformity in infrared focal-plane array sensors from scene data. The gain and the bias of each detector are regarded as random state variables modeled by a discrete-time Gauss–Markov process. The proposed Gauss–Markov framework provides a mechanism for capturing the slow and random drift in the fixed-pattern noise as the operational conditions of the sensor vary in time. With a temporal stochastic model for each detector’s gain and bias at hand, a Kalman filter is derived that uses scene data, comprising the detector’s readout values sampled over a short period of time, to optimally update the detector’s gain and bias estimates as these parameters drift. The proposed technique relies on a certain spatiotemporal diversity condition in the data, which is satisfied when all detectors see approximately the same range of temperatures within the periods between successive estimation epochs. The performance of the proposed technique is thoroughly studied, and its utility in mitigating fixed-pattern noise is demonstrated with both real infrared and simulated imagery.

In this paper a low-cost, practical pixel-based flame spectrum and temperature estimation system based on flame color images is proposed. A spectral resolution of ∼ 0.4 nm is achieved with an optical system formed by a color camera, a linear model, a flame's spectral training data, and a spectral reconstruction procedure. As a proof of concept, the estimated spectra are compared to local measurements performed with a commercial spectrometer. In order to estimate the absolute flame-temperature maps, two radiometric images at different wavelengths are reconstructed and the two-color pyrometry method is applied. Experiments show errors of about 2.0% over the estimated temperature, making this system a practical tool for flame sensing in combustion-process monitoring.

We have analyzed observations from a radioastronomical experiment to survey the sky at decimetric wavelengths along with feed pattern measurements in order to account for the level of ground contamination entering the sidelobes. A major asset of the experiment is the use of a wire mesh fence around the rim-halo shielded antenna with the purpose of levelling out and reducing this source of stray radiation for zenith-centered 1-rpm circular scans. We investigate the shielding performance of the experiment by means of a geometric diffraction model in order to predict the level of the spillover and diffraction sidelobes in the direction of the ground. Using 408 MHz and 1465 MHz feed measurements, the model shows how a weakly-diffracting and unshielded antenna configuration becomes strongly-diffracting and double-shielded as far-field diffraction effects give way to near-field ones. Due to the asymmetric response of the feeds, the orientation of their radiation fields with respect to the secondary must be known a priori before comparing model predictions with observational data. By adjusting the attenuation coefficient of the wire mesh the model is able to reproduce the amount of differential ground pick-up observed during test measurements at 1465 MHz., 14 pages, 17 eps + 1 gif figures and 4 Tables. Accepted for publication in A&AS. Fig.7 available at full resolution from http://www.das.inpe.br/~tello/publications.htm

High-speed cameras are reliable alternatives for the direct characterization of optical trap force and particle motion in optical tweezers setups, replacing indirect motion measurements often performed by quadrant detectors. In the present approach, subpixel motion data of the trapped particle is retrieved from a high-speed low-resolution video sequence. Due to the richness structure of motion diversity of microscopic trapped particles, which are subjected to a Brownian motion, we propose to also use the obtained motion information for tackling the inherent lack of resolution by applying superresolution algorithms on the low-resolution image sequence. The obtained results both for trapping calibration beads and for living bacteria show that the proposed approach allows the proper characterization of the optical tweezers by obtaining the real particle motion directly from the image domain, while still providing high resolution imaging.

A nonintrusive low-cost sensor based on silicon photodiode detectors has been designed to analyze the formation and behavior of excited CH* and C-2* radicals in the combination process by sensing the spectral emission of hydrocarbon flames. The sensor was validated by performing two sets of experiments for both nonconfined and confined flames. For a nonconfined oil flame, the sensor responses for the axial intensity were highly correlated with the measurements obtained with a radiometer. For confined gas flames the ratio between the signal corresponding to C-2* and CH* was successfully correlated with the CO Pollutant emissions and the combustion efficiency. These results give additional insight on how to prevent an incomplete combustion using spectral information. The fast response, the nonintrusive character, and the instantaneous measurement of the needed spectral information makes the proposed optical sensor a key element in the development of advanced control stratergies for combustion processes. (C) 2008 Optical Society of America

The spatial fixed-pattern noise (FPN) inherently generated in infrared (IR) imaging systems compromises severely the quality of the acquired imagery, even making such images inappropriate for some applications. The FPN refers to the inability of the photodetectors in the focal-plane array to render a uniform output image when a uniform-intensity scene is being imaged. We present a noise-cancellation-based algorithm that compensates for the additive component of the FPN. The proposed method relies on the assumption that a source of noise correlated to the additive FPN is available to the IR camera. An important feature of the algorithm is that all the calculations are reduced to a simple equation, which allows for the bias compensation of the raw imagery. The algorithm performance is tested using real IR image sequences and is compared to some classical methodologies.

A statistical algorithm has been developed to compensate for the fixed-pattern noise associated with spatial nonuniformity and temporal drift in the response of focal-plane array infrared imaging systems. The algorithm uses initial scene data to generate initial estimates of the gain, the offset, and the variance of the additive electronic noise of each detector element. The algorithm then updates these parameters by use of subsequent frames and uses the updated parameters to restore the true image by use of a least-mean-square error finite-impulse-response filter. The algorithm is applied to infrared data, and the restored images compare favorably with those restored by use of a multiple-point calibration technique.

We propose a novel approach for using a high-speed camera for the characterization of optical tweezers by registering the trapped particle subpixel motion, while still providing simultaneous high resolution imaging by using multiframe superresolution.