Projection-based image registration in the presence of fixed-pattern noise

Fixed Pattern Noise Removal Based on a Semi-Calibration Method

Due to the manufacturing imperfections, nonuniformities are ubiquitous in digital sensors, causing the notorious Fixed Pattern Noise (FPN). The ability of modern digital cameras to take images under low-light environments is severely limited by the FPN. This paper proposes a novel semi-calibration-based method for the FPN removal that utilizes a pre-calibrated Noise Pattern. The key observation of this work is that the FPN in each shot is actually a scaled Noise Pattern with an unknown scale parameter, since each pixel in the array generates a characteristic amount of dark current which is fundamentally determined by its physical properties. Given a noised image and the corresponding Noise Pattern, the scale parameter is automatically estimated, and then the FPN is removed by subtracting the scaled Noise Pattern from the noised image. The estimation of the scale parameter is based on an entropy minimization estimator, which is derived from the Maximum Likelihood principle and is further justified by subsequent analysis that minimizing the entropy uniquely identifies the true parameter. Convergence issues, as well as the optimality of the proposed estimator, are also theoretically discussed. Finally, some applications are given, illustrating the performance of the proposed FPN removal method in real-world tasks.

High-precision surface measurement with an automated multiangle low coherence interferometer

We propose a novel measurement system based on a low coherence Michelson interferometer and six-axis hexapod platform to accurately measure structures with high aspect ratio using different tilt angles of the measured surface. In order to realize automatic measurement, the system is designed to automatically perform autofocusing, adjust the tilt angle of the test surface, make surface measurements, and merge the measurement data sets. Due to certain topography, e.g., structures with high aspect ratio, the interferometer cannot obtain enough reflected light to evaluate the height information in some areas of the test surface. For this reason, we developed a measurement system that uses measurements from different tilt angles of the test surface and stitching algorithms to realize a complete surface measurement data set. The performance of the proposed measurement system is evaluated experimentally and compared to the results of measurements using a perthometer.

Dissociating the neural mechanisms of distance and spatial reference frames

This study investigated if the neural mechanisms involved in processing distance (near and far) and frame of reference (egocentric and allocentric) can be dissociated. 36 participants completed a conjunction visual search task using either an egocentric (deciding if the target was to their left or right) or an allocentric (deciding if the target was to the left or right of a reference object) frame. Both tasks were performed in near (57 cm) and far (171 cm) space conditions. Participants were separated into three groups, and each received transcranial magnetic stimulation (TMS) to a different site; right posterior parietal cortex (rPPC), right ventral occipital cortex (rVO), or right frontal eye field (rFEF) in addition to sham TMS. The results show that rFEF is critical in the processing of each search at each distance whereas, contrary to previous detection results, TMS over rVO did not affect performance for any condition. TMS over rPPC revealed that specialised egocentric processing in the parietal cortex does not generalise to far space, providing evidence of a separation of the reference frame/distance conflation in the literature.

Noise parameter estimation for poisson corrupted images using variance stabilization transforms

Noise is present in all images captured by real-world image sensors. Poisson distribution is said to model the stochastic nature of the photon arrival process and agrees with the distribution of measured pixel values. We propose a method for estimating unknown noise parameters from Poisson corrupted images using properties of variance stabilization. With a significantly lower computational complexity and improved stability, the proposed estimation technique yields noise parameters that are comparable in accuracy to the state-of-art methods.

Recursive algorithms for bias and gain nonuniformity correction in infrared videos

Infrared focal-plane array (IRFPA) detectors suffer from fixed-pattern noise (FPN) that degrades image quality, which is also known as spatial nonuniformity. FPN is still a serious problem, despite recent advances in IRFPA technology. This paper proposes new scene-based correction algorithms for continuous compensation of bias and gain nonuniformity in FPA sensors. The proposed schemes use recursive least-square and affine projection techniques that jointly compensate for both the bias and gain of each image pixel, presenting rapid convergence and robustness to noise. The synthetic and real IRFPA videos experimentally show that the proposed solutions are competitive with the state-of-the-art in FPN reduction, by presenting recovered images with higher fidelity.

Mono- and multimodal registration of optical breast images

Optical breast imaging offers the possibility of noninvasive, low cost, and high sensitivity imaging of breast cancers. Poor spatial resolution and a lack of anatomical landmarks in optical images of the breast make interpretation difficult and motivate registration and fusion of these data with subsequent optical images and other breast imaging modalities. Methods used for registration and fusion of optical breast images are reviewed. Imaging concerns relevant to the registration problem are first highlighted, followed by a focus on both monomodal and multimodal registration of optical breast imaging. Where relevant, methods pertaining to other imaging modalities or imaged anatomies are presented. The multimodal registration discussion concerns digital x-ray mammography, ultrasound, magnetic resonance imaging, and positron emission tomography.

Rigid registration of medical images using 1D and 2D binary projections

Image registration is a necessary procedure in everyday clinical practice. Several techniques for rigid and non-rigid registration have been developed and tested and the state-of-the-art is evolving from the research setting to incorporate image registration techniques into clinically useful tools. In this paper, we develop a novel rigid medical image registration technique which incorporates binary projections. This technique is tested and compared to the standard mutual information (MI) methods. Results show that the method is significantly more accurate and robust compared to MI methods. The accuracy is well below 0.5° and 0.5 mm. This method introduces two more improvements over MI methods: (1)for 2D registration with the use of 1D binary projections, we use minimal interpolation; and (2) for 3D registration with the use of 2D binary projections the method converges to stable final positions, independent of the initial misregistration.

Scene-based nonuniformity correction and enhancement: pixel statistics and subpixel motion

We propose a framework for scene-based nonuniformity correction (NUC) and nonuniformity correction and enhancement (NUCE) that is required for focal-plane array-like sensors to obtain clean and enhanced-quality images. The core of the proposed framework is a novel registration-based nonuniformity correction super-resolution (NUCSR) method that is bootstrapped by statistical scene-based NUC methods. Based on a comprehensive imaging model and an accurate parametric motion estimation, we are able to remove severe/structured nonuniformity and in the presence of subpixel motion to simultaneously improve image resolution. One important feature of our NUCSR method is the adoption of a parametric motion model that allows us to (1) handle many practical scenarios where parametric motions are present and (2) carry out perfect super-resolution in principle by exploring available subpixel motions. Experiments with real data demonstrate the efficiency of the proposed NUCE framework and the effectiveness of the NUCSR method.

Comparison of diffuse optical tomography of human breast with whole-body and breast-only positron emission tomography

We acquire and compare three-dimensional tomographic breast images of three females with suspicious masses using diffuse optical tomography (DOT) and positron emission tomography (PET). Co-registration of DOT and PET images was facilitated by a mutual information maximization algorithm. We also compared DOT and whole-body PET images of 14 patients with breast abnormalities. Positive correlations were found between total hemoglobin concentration and tissue scattering measured by DOT, and fluorodeoxyglucose (18F-FDG) uptake. In light of these observations, we suggest potential benefits of combining both PET and DOT for characterization of breast lesions.

Standardized platform for coregistration of nonconcurrent diffuse optical and magnetic resonance breast images obtained in different geometries

We present a novel methodology for combining breast image data obtained at different times, in different geometries, and by different techniques. We combine data based on diffuse optical tomography (DOT) and magnetic resonance imaging (MRI). The software platform integrates advanced multimodal registration and segmentation algorithms, requires minimal user experience, and employs computationally efficient techniques. The resulting superposed 3-D tomographs facilitate tissue analyses based on structural and functional data derived from both modalities, and readily permit enhancement of DOT data reconstruction using MRI-derived a-priori structural information. We demonstrate the multimodal registration method using a simulated phantom, and we present initial patient studies that confirm that tumorous regions in a patient breast found by both imaging modalities exhibit significantly higher total hemoglobin concentration (THC) than surrounding normal tissues. The average THC in the tumorous regions is one to three standard deviations larger than the overall breast average THC for all patients.

Binary weighted averaging of an ensemble of coherently collected image frames

Recent interest in the collection of remote laser radar imagery has motivated novel systems that process temporally contiguous frames of collected imagery to produce an average image that reduces laser speckle, increases image SNR, decreases the deleterious effects of atmospheric distortion, and enhances image detail. This research seeks an algorithm based on Bayesian estimation theory to select those frames from an ensemble that increases spatial resolution compared to simple unweighted averaging of all frames. The resulting binary weighted motion-compensated frame average is compared to the unweighted average using simulated and experimental data collected from a fielded laser vision system. Image resolution is significantly enhanced as quantified by the estimation of the atmospheric seeing parameter through which the average image was formed.

A projection-based image registration algorithm and its application

Proposed is a projection-based image registration technique where, by rearranging the projections of characteristic images, the image registration is implemented with two independent steps - rotation and translation, to perform the two-dimensional or three-dimensional rigid-body image registration addressing the head motion problem in functional magnetic resonance imaging (fMRI). For a 2D problem, the approach is based on a one-dimensional projection of a segmented two-dimensional characteristic image, in which the translation and rotation parameters are obtained with a one-dimensional cross-correlation-based estimator. This is then used to compute the cross-correlation between the projection of an image and a registration table that is created by rearranged projections of a selected two-dimensional image with various rotation angles. In this approach, the translation registration table may be created by rearranged projections of sub-voxel level two-dimensional images with various sub-voxel level parameters, and so it may be applied into a sub-voxel registration. Such an approach replaced the general multi-dimensional optimization procedure with a linear projection calculation and a finite cross-correlation with a registration table, thus the amount of computation is considerably reduced. The performance of this method was confirmed by simulation study different SNRs and applications to 2D and 3D actual functional MRI images.

An algebraic algorithm for nonuniformity correction in focal-plane arrays

A scene-based algorithm is developed to compensate for bias nonuniformity in focal-plane arrays. Nonuniformity can be extremely problematic, especially for mid- to far-infrared imaging systems. The technique is based on use of estimates of interframe subpixel shifts in an image sequence, in conjunction with a linear-interpolation model for the motion, to extract information on the bias nonuniformity algebraically. The performance of the proposed algorithm is analyzed by using real infrared and simulated data. One advantage of this technique is its simplicity; it requires relatively few frames to generate an effective correction matrix, thereby permitting the execution of frequent on-the-fly nonuniformity correction as drift occurs. Additionally, the performance is shown to exhibit considerable robustness with respect to lack of the common types of temporal and spatial irradiance diversity that are typically required by statistical scene-based nonuniformity correction techniques.