KEYWORDS: Reconstruction algorithms, X-rays, Grazing incidence, Algorithms, Dual energy imaging, Signal attenuation, Tomography, X-ray imaging, Data acquisition, Systems modeling
In dual energy computed tomography (DECT), two sets of projection data are acquired using a couple of
independent X-ray spectra. Since the attenuation characteristic of a material without a K-edge in a typical
medical X-ray spectrum range is accurately described by the linear combination of two phenomena, which are
the photoelectric attenuation and the Compton scatter, the DECT is theoretically capable of separating one
material from another. However, the material decomposition (MD) is still a challenging problem in DECT, since
two sets of sinograms from distinct X-ray spectra are not spatially aligned in practices. To avoid this problem, the
MD is often achieved by a weighted summation of two reconstructed volumes that correspond to a couple of sets
of projection data, which the monochromatic approximation is generally used in the reconstruction procedure.
The accuracy of the MD, therefore, can be limited due to the erroneous ignorance of the energy dependency of the
acquisition model. In this paper, we propose a novel algorithm, named information theoretic discrepancy based
iterative reconstruction (IDIR) algorithm, for an accurate MD in dual energy X-ray systems. The generalized
information theoretic discrepancy (GID) measure is newly employed as the objective value. Using particular
features of the GID, a tractable objective function for the
material-selective reconstruction is derived, which
accounts the exact polychromatic model of transmission tomography. Since the spectral model of measured data
is explicitly considered, the accurate MD is possible even for misaligned projections. In numerical experiments,
the proposed method showed superior reconstruction performance over the conventional approach.
The X-ray tomosynthesis that measures several low dose projections over a limited angular range has been investigated
as an alternative method of X-ray mammography for breast cancer screening. An extension of the scan
coverage increases the vertical resolution by mitigating the interplane blurring. The implementation of a wide
angle tomosynthesis equipment, however, may not be straightforward, mainly due to the image deterioration
from the statistical noise in exterior projections. In this paper, we adopt the voltage modulation scheme to
enlarge the coverage of the tomosynthesis scan. The higher tube voltages are used for outer angles, which offers
the sufficient penetrating power for outlying frames in which the pathway of X-ray photons is elongated. To
reconstruct 3D information from voltage modulated projections, we propose a novel algorithm, named information
theoretic discrepancy based iterative reconstruction (IDIR) algorithm, which allows to account for the
polychromatic acquisition model. The generalized information theoretic discrepancy (GID) is newly employed as
the objective function. Using particular features of the GID, the cost function is derived in terms of imaginary
variables with energy dependency, which leads to a tractable optimization problem without using the monochromatic
approximation. In preliminary experiments using simulated and experimental equipment, the proposed
imaging architecture and IDIR algorithm showed superior performances over conventional approaches.
The X-ray mammography is the primary imaging modality for breast cancer screening. For the dense breast,
however, the mammogram is usually difficult to read due to tissue overlap problem caused by the superposition
of normal tissues. The digital breast tomosynthesis (DBT) that measures several low dose projections over a
limited angle range may be an alternative modality for breast imaging, since it allows the visualization of the
cross-sectional information of breast. The DBT, however, may suffer from the aliasing artifact and the severe noise
corruption. To overcome these problems, a total variation (TV) regularized statistical reconstruction algorithm
is presented. Inspired by the dual formulation of TV minimization in denoising and deblurring problems, we
derived a gradient-type algorithm based on statistical model of X-ray tomography. The objective function is
comprised of a data fidelity term derived from the statistical model and a TV regularization term. The gradient
of the objective function can be easily calculated using simple operations in terms of auxiliary variables. After
a descending step, the data fidelity term is renewed in each iteration. Since the proposed algorithm can be
implemented without sophisticated operations such as matrix inverse, it provides an efficient way to include the
TV regularization in the statistical reconstruction method, which results in a fast and robust estimation for low
dose projections over the limited angle range. Initial tests with an experimental DBT system confirmed our
finding.
KEYWORDS: Image processing, Image quality, Digital cameras, Digital image processing, Cameras, Signal processing, RGB color model, Image fusion, Image sensors, Signal to noise ratio
Market's demands of digital cameras for higher sensitivity capability under low-light conditions are remarkably
increasing nowadays. The digital camera market is now a tough race for providing higher ISO capability. In this paper,
we explore an approach for increasing maximum ISO capability of digital cameras without changing any structure of an
image sensor or CFA. Our method is directly applied to the raw Bayer pattern CFA image to avoid non-linearity
characteristics and noise amplification which are usually deteriorated after ISP (Image Signal Processor) of digital
cameras. The proposed method fuses multiple short exposed images which are noisy, but less blurred. Our approach is
designed to avoid the ghost artifact caused by hand-shaking and object motion. In order to achieve a desired ISO image
quality, both low frequency chromatic noise and fine-grain noise that usually appear in high ISO images are removed
and then we modify the different layers which are created by a two-scale non-linear decomposition of an image. Once
our approach is performed on an input Bayer pattern CFA image, the resultant Bayer image is further processed by ISP
to obtain a fully processed RGB image. The performance of our proposed approach is evaluated by comparing SNR
(Signal to Noise Ratio), MTF50 (Modulation Transfer Function), color error ∝E*ab and visual quality with reference
images whose exposure times are properly extended into a variety of target sensitivity.
KEYWORDS: Image enhancement, Image processing, RGB color model, Cameras, High dynamic range imaging, Image restoration, Digital imaging, Image sensors, Digital cameras, Linear filtering
In many cases, it is not possible to faithfully capture shadow and highlight image data of a high dynamic range (HDR)
scene using a common digital camera, due to its narrow dynamic range (DR). Conventional solutions tried to solve the
problem with an captured image which has saturated highlight and/or lack of shadow information. In this situation, we
introduce a color image enhancing method with the scene-adaptive exposure control. First, our method recommends an
optimal exposure to obtain more information in highlight by the histogram-based scene analysis. Next, the proposed
luminance and contrast enhancement is performed on the captured image. The main processing consists of luminance
enhancement, multi-band contrast stretching, and color compensation. The luminance and chrominance components of
input RGB data is separated by converting into HSV color space. The luminance is increased using an adaptive log
function. Multi-band contrast stretching functions are applied to each sub-band to enhance shadow and highlight at the
same time. To remove boundary discontinuities between sub-bands, the multi-level low-pass filtering is employed. The
blurred image data represents local illumination while the contrast-stretched details correspond to reflectance of the
scene. The restored luminance image is produced by the combination of multi-band contrast stretched image and multilevel
low-pass filtered image. Color compensation proportional to the amount of luminance enhancement is applied to
make an output image.
Dynamic range of natural scenes that we see in daily life ranges up to 120dB. Unfortunately, typical imaging devices
only cover about 50dB without any special circuit technique. To overcome this dynamic range problem, many algorithms
and devices have been developed and commercialized. However, commercialization in the field where image quality is
emphasized is not as active as in the filed where the image information is emphasized. This is because there are still
some limitations in capturing and displaying high dynamic range (HDR) image without loss of image information (color,
edge and contrast, etc.) In displaying HDR image, some losses of image information during tone reproduction are
inevitable, since the HDR image have to be processed with some kind of tone reproduction method to compress the
dynamic range fit to the low dynamic range (LDR) display devices. Also there is a report that the tone reproduced LDR
image on LDR display device is viewed better than HDR image on HDR display according to Oh [1]. For this reason, we
propose a new method which can tonally reproduce HDR image into a natural LDR image as auto exposed (AE) one
with minimum loss of image information.
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