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A reference-free image index to jointly assess infrared-imaging fixed-pattern-noise and blur artifacts is proposed in this work. The proposed index is based on tuned-spatial-domain filtering, which works by combining two Laplace operators to simultaneously quantify the global infrared-imaging fixed-pattern-noise and the global or local blur artifacts. The index effectiveness is demonstrated by two task-based image-quality assessments to determine the focused and fixed-pattern-noise free images from sequences captured with both a mid-wave-infrared microscope system and a long-wave-infrared plenoptic system. The index quantitative limits are shown on numerical computations over synthetic corrupted images as well as real black-body radiator calibrated infrared images with representative simulated fixed-pattern noise, from six well known infrared focal plane arrays transducer technologies, along with artificial blur added using real infrared imaging system point spread functions.

Infrared (IR) imaging systems are known to have a range of sensor and optical limitations that result in degraded imagery. Fixed pattern noise (FPN), resulting from pixel-to-pixel response nonuniformity, is a dominant source of error that manifests in collected imagery through the appearance of temporally and spatially correlated noise patterns that are mixed with each image. Furthermore, finite detector size coupled with imperfect system optics can introduce blurring effects and aliasing, ultimately reducing resolution in acquired images. Here, we propose a unified method to reduce FPN and recover high-frequency image content in IR microscopy images. The proposed method uses regularized nonlocal means to highlight spatial features in the scene while maintaining fine textural image details. We derive an iterative optimization method based upon a gradient descent minimization strategy that applies a Wiener deconvolution in each iteration to estimate the blur artifacts. The method is implemented within an embedded mid-wave IR imaging system for microscopy applications. We demonstrate a reduction in FPN and blurring artifacts, achieving improved image resolution in the reconstructed images that are apparent in recovered details on scene objects.

In this work, we present a new technique to simultaneously reduce two major degradation artifacts found in mid-wavelength infrared microscopy imagery, namely the inherent focal-plane array nonuniformity noise and the scene defocus presented due to the point spread function of the infrared microscope. We correct both nuisances using a novel, recursive method that combines the constant range nonuniformity correction algorithm with a frame-by-frame deconvolution approach. The ability of the method to jointly compensate for both nonuniformity noise and blur is demonstrated using two different real mid-wavelength infrared microscopic video sequences, which were captured from two microscopic living organisms using a Janos-Sofradir mid-wavelength infrared microscopy setup. The performance of the proposed method is assessed on real and simulated infrared data by computing the root mean-square error and the roughness-laplacian pattern index, which was specifically developed for the present work.

The present work proposes a method for the 3-D full-field focusing for microscopic infrared (IR) imagery. It is based on the partial analysis of Point Spread Function (PSF) for a confined volumetric universe of vision in a microscopic IR system. The ability of the algorithm to compensate for localized blur is demonstrated using two different real MWIR microscopic video sequences, which were 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 infrared data by computing the root mean-square error and the roughness-laplacian pattern, which was specifically developed for the present work.

In this paper, two novel multidimensional striping noise compensation (SNC) algorithms for push-broom hyperspectral cameras (PBHCs) have been developed. The SNC algorithms employ a novel pixelwise, affine image-degradation model, which assumes that the striping noise (SN) parameters are spatially uncorrelated, spectrally independent, and decoupled from the camera’s spectral response. Algorithms simultaneously exploit the spatial and temporal information contained in an image as well as the spectral information contained at adjacent spectral images. The multidimensional SNC algorithms were successfully tested on real hyperspectral data from both a commercial PBHC operating in the spectral range of 400–1000 nm, at a resolution of 1.04 nm, and the ESA earth-observing CHRIS/PROBA sensor.

In this work, a machine vision system for the automatic online detection of the parasite Edotea magellanica inside of the clam Mulinia edulis is presented. The machine vision system uses a transillumination technique to acquire images of the clam-parasite tandem. To improve the light transmission properties of the clam and parasite tissues, a novel online flattening system to flatten the clam thickness was developed. The automatic detection of the parasite in a clam is accomplished by a binary decision tree classifier that analyzes clam images. The classifier was developed using a supervised pattern-recognition approach, and proper features were created using spectral, spatial, geometrical, and biological information, such as the relative transmittance, the location of the parasite in the clam, the shape of the parasite, and the anatomical regions of the clam. The prototype system was built and tested, achieving an average classification accuracy of 98%, for a laboratory training sample set of 200 clams, and an accuracy of 73% for another set of 200 clams in an industrial setting.

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 paper, we propose a unified method to reduce infrared optoelectronic imaging degradations and perform digital superresolution from a sequence of infrared microscopy images. The proposed method combines two regulators highlighting the spatial features of the scene, maintaining fine texture image details and better preserving sharp edges. Further, the method compensates the presence of fixed-pattern noise and used a deconvolution filter to reduce blurring. The method is implemented on a built-in mid-wave infrared microscopy imaging system. The results show a significant reduction in the imaging nonuniformity and blur, achieving better digital resolution identifying more details of the different scene object thermal patterns.

Digital photonic sensors have greatly evolved to maximize sensitivity and spatial, spectral, and temporal imaging resolution. For low-energy photons, new designs have generated new types of noise that degrade the formed-image signal-to-noise ratio to values lower than 1. Fixed-pattern noise (FPN), which is produced by the non-uniform focal-plane-array optoelectronics response, is an ill-posed problem in infrared and hyperspectral imaging science. Here, we experimentally show that the FPN behaves as an object at a depth of infinity when a light field is captured by an imaging system. The proposed method is based on the capture of the light field of a scene and digital refocusing to any nearby objects in the scene. Unlike standard techniques for FPN reduction, our method does not require knowledge of the physical parameters of the optoelectronic transducer, the motion scene, or the presence of off-line blackbody sources. The ability of the proposed method to reduce FPN is measured by evaluating the structural similarity (SSIM) index employing a blackbody-based FPN reduction technique as a reference. This new interpretation of the FPN opens avenues to create new cameras for low-energy photons with the ability to perform denoising by digital refocusing.

In an earlier work, a recursive filter to compensate for the offset nonuniformity (NU) noise corrupting the output of infrared (IR) imaging system was presented. Such a filter was derived assuming an estimation time-window short enough so that the offset NU can be regarded as a constant corrupted by additive noise. In this paper, the assumption on the stationarity of the offset NU noise has been relaxed by characterizing the dynamics of the offset NU using a Gauss-Markov random process. Based upon this model, a recursive filter that uses blocks of IR data to estimate the offset NU has been derived. A rigorous theoretical analysis of the filter has been conducted in order to provide expressions for appropriately selecting the parameters of the filter. The ability of the block-recursive filter to compensate for NU noise has been tested using raw IR videos from different IR cameras. In addition, the filter has been implemented and tested on-line in a prototype spectrally tunable IR camera.

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.

In this paper, a prior knowledge model is proposed in order to increase the effectiveness of a multidimensional striping noise compensation (SNC) algorithm. This is accomplished by considering an optoelectronic approach, thereby generating a more accurate mathematical representation of the hyperspectral acquisition process. The proposed model includes knowledge on the system spectral response, which can be obtained by means of an input with known spectral radiation. Further, the model also considers the dependence of the noise structure on the analog-digital conversion process, that is, schemes such as active-pixel sensor (APS) and passive-pixel sensor (PPS) have been considered. Finally, the model takes advantage of the degree of crosstalk between consecutive bands in order to determinate how much of this spectral information is contributing to the read out data obtained in a particular band. All prior knowledge is obtained by a series of experimental analysis, and then integrated into the model. After estimating the required parameters, the applicability of the multidimensional SNC is illustrated by compensating for stripping noise in hyperspectral images acquired using an experimental setup. A laboratory prototype, based on both a Photonfocus Hurricane hyperspectral camera and a Xeva Xenics NIR hyperspectral camera, has been implemented to acquire data in the range of 400-1000 [nm] and 900-1700 [nm], respectively. Also, a mobile platform has been used to simulate and synchronize the scanning procedure of the cameras and an uniform tungsten lamp has been installed to ensure an equal spectral radiance between the different bands for calibration purpose.

Quality control of clams considers th e detection of foreign objects like s hell pieces, sand and even parasites.Particularly, Mulinia edulis clams are susceptible to have a parasi te infection caused by the isopoda Edoteamagellanica , which represents a serious commercial problem commonly addressed by manual inspection. In thiswork a machine vision system capable of automatically detect the parasite using a clam image is presented. Theparasite visualization inside the clam is achieved by an optoelectronic imaging system based on an transillumi-nation technique. Furthermore, automatic parasite detection in the clams image is accomplished by a patternrecognition system designed to quantitatively describe p arasite candidate zones. The extracted features are usedto predict the parasite presence by means of a binary deci sion tree classi“er. A real sample dataset of more than155000 patterns of parasite candidate zones was generated using 190 shell-o cooked clams from the Chileansouth paci“c coasts. This data collection was used to train a test the classi“er using cross-validation. Primaryresults have shown a mean parasite detection rate of 85% and a mean total correct classi“cation of 87%, whichrepresent a substantive improvement to the existing solutions.Keywords: parasite detection, clams, machine vision, optical inspection, image processing, pattern classi“cation

Algorithms for striping noise compensation (SNC) for push-broom hyperspectral cameras (PBHCs) are primarily based on image processing techniques. These algorithms rely on the spatial and temporal information available at the readout data; however, they disregard the large amount of spectral information also available at the data. In this paper such flaw has been tackled and a multidimensional approach for SNC is proposed. The main assumption of the proposed approach is the short-term stationary behavior of the spatial, spectral, and temporal input information. This assumption is justified after analyzing the optoelectronic sampling mechanism carried out by PBHCs. Namely, when the wavelength-resolution of hyperspectral cameras is high enough with respect to the target application, the spectral information at neighboring photodetectors in adjacent spectral bands can be regarded as a stationary input. Moreover, when the temporal scanning of hyperspectral information is fast enough, consecutive temporal and spectral data samples can also be regarded as a stationary input at a single photodetector. The strength and applicability of the multidimensional approach presented here is illustrated by compensating for stripping noise real hyperspectral images. To this end, a laboratory prototype, based on a Photonfocus Hurricane hyperspectral camera, has been implemented to acquire data in the range of 400-1000 [nm], at a wavelength resolution of 1.04 [nm]. A mobile platform has been also constructed to simulate and synchronize the scanning procedure of the camera. Finally, an image-processing-based SNC algorithm has been extended yielding an approach that employs all the multidimensional information collected by the camera.

In this work a closed-form, maximum-likelihood (ML) estimator for the variance of the thermal noise in magnetic resonance imaging (MRI) systems has been developed. The ML estimator was, in turn, used as a priori information for devising a single dimensional noise-cancellation–based image restoration algorithm. The performance of the estimator was assessed theoretically by means of the Cramer-Rao lower bound, and the effect of selecting an appropriate set of no-signal pixels on estimating the noise variance was also investigated. The effectivity of the noise-cancellation–based image restoration algorithm in compensating for the thermal noise in MRI was also evaluated. Actual MRI data from the LONI database was employed to assess the performance of both the ML estimator and the image restoration algorithm.

We propose an adaptive scene-based nonuniformity correction method based on the minimization of the total variation of the estimated irradiance, which provides enhanced results in both simulated and real infrared imagery, showing less ghosting artifacts.

Fixed-pattern noise is a common feature in several uncalibrated imaging systems, and it typically appears as striping and grid-like nonuniformity artifacts in hyperspectral and infrared cameras. In this work, we present a quantitative and comparative analysis of fixed-pattern noise reduction, or calibrating techniques, by using several image quality indexes. A special emphasis is made in demonstrating the correspondence between the reference-free (blind) image quality indexes and the typical reference-based metrics, specially when using online calibration procedures where reference data is not available. We evaluate the performance of several classic scene-based calibrating algorithms applied to: multispectral images with simulated striping noise; and infrared image sequences with simulated nonuniformity. The results show that most of the tested reference-free indexes are useful indicators for tracking some of the real degradation of the calibrated or even uncalibrated imagery, but they are far from perfect to match an error or similarity measure if the clean or reference data is available.

In order to explore new patterns for classification of cardiac signals, taken from the electrocardiogram (ECG), the circular statistic approach is introduced. Features are extracted from instantaneous phase of ECG signal using the analytic signal model based on the Hilbert transform theory. Feature vectors are used as patterns to distinguish among different ECG signals. Five types of ECG signals are obtained from MIT-BIH database. Preliminar results shown that the proposed features can be used on ECG signal classification problem.

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, we proposed a model based correlation measure between gain and offset nonuniformity for infrared focal plane array (FPA) imaging systems. Actually, several nonuniformity correction methods perform correction of nonuniformities by means of gain and off-set estimation in a detector-by-detector basis using several approach such as laboratory calibration methods, registration-based algorithm, and algebraic and statistical scene-based algorithm. Some statistical algorithms model the slow and random drift in time that the gain and offset present in many practical FPA applications by means of Gauss-Markov model, assuming that the gain and offset are uncorrelated. Due to this, in this work we present a study and model of such correlation by means of a generalized Gauss-Markov model. The gain and offset model-based correlation is validate using several infrared video sequences.

In this paper we present the effective-roughness (ERo) index, a simple reference-free performance metric for nonuniformity correction methods that agrees with visual evaluations.

In this paper, a novel color space transform is presented. It is an adaptive transform based on the application of independent component analysis to the RGB data of an entire color image. The result is a linear and reversible color space transform that provides three new coordinate axes where the projected data is as much as statistically independent as possible, and therefore highly uncorrelated. Compared to many non-linear color space transforms such as the HSV or CIE-Lab , the proposed one has the advantage of being a linear transform from the RGB color space, much like the XYZ or YIQ . However, its adaptiveness has the drawback of needing an estimate of the transform matrix for each image, which is sometimes computationally expensive for larger images due to the common iterative nature of the independent component analysis implementations. Then, an image subsampling method is also proposed to enhance the novel color space transform speed, efficiency and robustness. The new color space is used for a large set of test color images, and it is compared to traditional color space transforms, where we can clearly visualize its vast potential as a promising tool for segmentation purposes for example.

In this paper, a technique to improve the convergence and to reduce the ghosting artifacts of a previously developed adaptive scene-based nonuniformity correction method is presented. The nonuniformity correction method estimates detector parameters based on the recursive least square filter approach. We propose, three parameters to reduce ghosting artifacts and to speed up the convergence of such method by using only the read-out data. The parameters proposed are based in identify global motion between consecutive frames as well as evaluate the main assumption used in the previous method in the uncertainty on the input infrared irradiance. The ability of the method to compensate for nonuniformity and reducing ghosting artifacts is demonstrated by employing several infrared video sequences obtained using two infrared cameras.

In this paper, an adaptive scene-based nonuniformity correction methodology for infrared image sequences is developed. The method estimates detector parameters and carry out the non-uniformity correction based on the recursive least square filter approach, with adaptive supervision. The key advantage of the method is based in its capacity for estimate detectors parameters, and then compensate for fixed-pattern noise in a frame by frame basics. The ability of the method to compensate for nonuniformity is demonstrated by employing several infrared video sequences obtained using two infrared cameras.

In this paper, a method to design separable trade-off correlation filters for optical pattern recognition is developed. The proposed method not only is able to include the information about de desirable peak correlation value but also is able to minimize both the average correlation energy and the effect of additive noise on the correlation output. These optimization criteria are achieved by employing multiple training objects. The main advantage of the method is based on using multiple information for improving the optical pattern recognition work on images with various objects. The separable Trade-off filter is experimentally tested by using both digital and optical pattern recognition.

In this paper, an adaptive scene-based nonuniformity and ghosting artifacts correction algorithm for infrared image sequences is presented. The method simultaneously estimates detector parameters and carry out the non-uniformity and ghosting artifacts correction based on the retina-like neural network approach. The method incorporates the use of a new adaptive learning rate rule into the estimation of the gain and the offset of each detector. This learning rule, together with the consideration of the dependence of the detector's parameters on the retinomorphic assumption used for parameter estimation, may sustain an efficient method that could not only increase the original method's ability for estimating the non-uniformity noise, but also increase the capability of mitigating ghosting artifacts. The ability of the method to compensate for nonuniformity and reducing ghosting artifacts is demonstrated by employing several infrared video sequences obtained using two infrared cameras.

A scene-based method for nonuniformity correction of infrared image sequences is developed and tested. The method uses the information embedded in the scene and performs the correction in a frame by frame Kalman Filter approach. The key assumption of the method is that the uncertainty on the input infrared irradiance integrated by each detector is solved using the spatial infrared information collected from the scene. The performance of the method is tested using infrared image sequences captured by two infrared cameras.

This paper presents a multiple model parallel processing technique to adaptively estimate the nonuniformity parameters of infrared image sequences. The approach is based on both an optimal recursive estimation based on a fast form of the Kalman filter, and a solution for the uncertainties on the system model by running a bank of those estimators in parallel. The residual errors of these estimators are used as hypothesis to test and assign the conditional probabilities of each model in the bank of the Information form of the Kalman filter. The conditional probabilities are used to calculate weighting factors for each estimation and to compute the final system state estimation as a weighted sum. Then, the weighting factors are updated recursively from one to another sequence of infrared images, providing to the estimator a way to follow the dynamic of the scene recorded by the infrared imaging system. The ability of the scheme to adaptively compensates nonuniformity in infrared imagery is demonstrated by using real infrared image sequences.

A statistical technique for adaptive nonuniformity correction of infrared image sequences has been developed. The method, which relies on our previously developed constant range nonuniformity correction method, estimates the nonuniformity parameters using two recursive estimation techniques. The method selects an estimation algorithm using a decision rule based on a threshold value computed from the collected infrared images. The strength of the method lies in its simplicity, low computational complexity, and its good trade-off between nonuniformity correction and ghosting artifacts reduction. The ability of the enhanced constant range technique to compensate for nonuniformity noise is demonstrated by using video sequences of infrared imagery with both real and synthetic nonuniformity.

The non-uniform response in infrared focal plane array (IRFPA) detectors produces corrupted images with a fixed-pattern noise. In this paper we present an enhanced adaptive scene-based non-uniformity correction (NUC) technique. The method simultaneously estimates detector's parameters and performs the non-uniformity compensation using a neural network approach. In addition, the proposed method doesn't make any assumption on the kind or amount of non-uniformity presented on the raw data. The strength and robustness of the proposed method relies in avoiding the presence of ghosting artifacts through the use of optimization techniques in the parameter estimation learning process, such as: momentum, regularization, and adaptive learning rate. The proposed method has been tested with video sequences of simulated and real infrared data taken with an InSb IRFPA, reaching high correction levels, reducing the fixed pattern noise, decreasing the ghosting, and obtaining an effective frame by frame adaptive estimation of each detector's gain and offset.

It is well known that non-uniformity noise in focal-plane array (FPA) sensors, which is due to the pixel-to-pixel variation in the detectors' responses, can considerably degrade the quality of images since it results in a fixed pattern that is superimposed on the true scene. This paper addresses the benefits of non-uniformity correction (NUC) pre- processing on the performance of a number of existing post- processing algorithms. The post-processing applications considered are image registration (motion compensation) and high-resolution image reconstruction from a sequence of blurred and undersampled images. The accuracy of motion estimation in the presence of non-uniformity noise is investigated in terms of the standard deviation of the gain and the offset of the FPA detectors. Two recently reported scene-based NUC techniques are employed to pre-process the FPA output prior post-processing. The first NUC technique is based on the assumption that all pixels experience the same range of irradiance over a sufficiently long sequence of frames (constant-range assumption), and the second NUC technique is registration-based and can be used recursively. It is shown that NUC improves the accuracy of registration, and consequently, enhances the benefit of high-resolution reconstruction. It is also shown that under cases of severe non-uniformity noise, a recursive application of the registration-based NUC may further improve the accuracy of registration and high-resolution image reconstruction. The results are illustrated using real and simulated data.