127 research outputs found
Real-time filtering and detection of dynamics for compression of HDTV
The preprocessing of video sequences for data compressing is discussed. The end goal associated with this is a compression system for HDTV capable of transmitting perceptually lossless sequences at under one bit per pixel. Two subtopics were emphasized to prepare the video signal for more efficient coding: (1) nonlinear filtering to remove noise and shape the signal spectrum to take advantage of insensitivities of human viewers; and (2) segmentation of each frame into temporally dynamic/static regions for conditional frame replenishment. The latter technique operates best under the assumption that the sequence can be modelled as a superposition of active foreground and static background. The considerations were restricted to monochrome data, since it was expected to use the standard luminance/chrominance decomposition, which concentrates most of the bandwidth requirements in the luminance. Similar methods may be applied to the two chrominance signals
Sub-band/transform compression of video sequences
The progress on compression of video sequences is discussed. The overall goal of the research was the development of data compression algorithms for high-definition television (HDTV) sequences, but most of our research is general enough to be applicable to much more general problems. We have concentrated on coding algorithms based on both sub-band and transform approaches. Two very fundamental issues arise in designing a sub-band coder. First, the form of the signal decomposition must be chosen to yield band-pass images with characteristics favorable to efficient coding. A second basic consideration, whether coding is to be done in two or three dimensions, is the form of the coders to be applied to each sub-band. Computational simplicity is of essence. We review the first portion of the year, during which we improved and extended some of the previous grant period's results. The pyramid nonrectangular sub-band coder limited to intra-frame application is discussed. Perhaps the most critical component of the sub-band structure is the design of bandsplitting filters. We apply very simple recursive filters, which operate at alternating levels on rectangularly sampled, and quincunx sampled images. We will also cover the techniques we have studied for the coding of the resulting bandpass signals. We discuss adaptive three-dimensional coding which takes advantage of the detection algorithm developed last year. To this point, all the work on this project has been done without the benefit of motion compensation (MC). Motion compensation is included in many proposed codecs, but adds significant computational burden and hardware expense. We have sought to find a lower-cost alternative featuring a simple adaptation to motion in the form of the codec. In sequences of high spatial detail and zooming or panning, it appears that MC will likely be necessary for the proposed quality and bit rates
Direct Iterative Reconstruction of Multiple Basis Material Images in Photon-counting Spectral CT
In this work, we perform direct material reconstruction from spectral CT data
using a model based iterative reconstruction (MBIR) approach. Material
concentrations are measured in volume fractions, whose total is constrained by
a maximum of unity. A phantom containing a combination of 4 basis materials
(water, iodine, gadolinium, calcium) was scanned using a photon-counting
detector. Iodine and gadolinium were chosen because of their common use as
contrast agents in CT imaging. Scan data was binned into 5 energy (keV) levels.
Each energy bin in a calibration scan was reconstructed, allowing the linear
attenuation coefficient of each material for every energy to be estimated by a
least-squares fit to ground truth in the image domain. The resulting matrix, for energies and materials, is incorporated into the forward
model in direct reconstruction of the basis material images with spatial
and/or inter-material regularization. In reconstruction from a subsequent
low-concentration scan, volume fractions within regions of interest (ROIs) are
found to be close to the ground truth. This work is meant to lay the foundation
for further work with phantoms including spatially coincident mixtures of
contrast materials and/or contrast agents in widely varying concentrations,
molecular imaging from animal scans, and eventually clinical applications
Statistically Adaptive Filtering for Low Signal Correction in X-ray Computed Tomography
Low x-ray dose is desirable in x-ray computed tomographic (CT) imaging due to
health concerns. But low dose comes with a cost of low signal artifacts such as
streaks and low frequency bias in the reconstruction. As a result, low signal
correction is needed to help reduce artifacts while retaining relevant
anatomical structures.
Low signal can be encountered in cases where sufficient number of photons do
not reach the detector to have confidence in the recorded data. % NOTE: SNR is
ratio of powers, not std. dev. X-ray photons, assumed to have Poisson
distribution, have signal to noise ratio proportional to the dose, with poorer
SNR in low signal areas. Electronic noise added by the data acquisition system
further reduces the signal quality.
In this paper we will demonstrate a technique to combat low signal artifacts
through adaptive filtration. It entails statistics-based filtering on the
uncorrected data, correcting the lower signal areas more aggressively than the
high signal ones. We look at local averages to decide how aggressive the
filtering should be, and local standard deviation to decide how much detail
preservation to apply. Implementation consists of a pre-correction step i.e.
local linear minimum mean-squared error correction, followed by a variance
stabilizing transform, and finally adaptive bilateral filtering. The
coefficients of the bilateral filter are computed using local statistics.
Results show that improvements were made in terms of low frequency bias,
streaks, local average and standard deviation, modulation transfer function and
noise power spectrum
MBIR Training for a 2.5D DL network in X-ray CT
In computed tomographic imaging, model based iterative reconstruction methods
have generally shown better image quality than the more traditional, faster
filtered backprojection technique. The cost we have to pay is that MBIR is
computationally expensive. In this work we train a 2.5D deep learning (DL)
network to mimic MBIR quality image. The network is realized by a modified
Unet, and trained using clinical FBP and MBIR image pairs. We achieve the
quality of MBIR images faster and with a much smaller computation cost.
Visually and in terms of noise power spectrum (NPS), DL-MBIR images have
texture similar to that of MBIR, with reduced noise power. Image profile plots,
NPS plots, standard deviation, etc. suggest that the DL-MBIR images result from
a successful emulation of an MBIR operator
2.5D Deep Learning for CT Image Reconstruction using a Multi-GPU implementation
While Model Based Iterative Reconstruction (MBIR) of CT scans has been shown
to have better image quality than Filtered Back Projection (FBP), its use has
been limited by its high computational cost. More recently, deep convolutional
neural networks (CNN) have shown great promise in both denoising and
reconstruction applications. In this research, we propose a fast reconstruction
algorithm, which we call Deep Learning MBIR (DL-MBIR), for approximating MBIR
using a deep residual neural network. The DL-MBIR method is trained to produce
reconstructions that approximate true MBIR images using a 16 layer residual
convolutional neural network implemented on multiple GPUs using Google
Tensorflow. In addition, we propose 2D, 2.5D and 3D variations on the DL-MBIR
method and show that the 2.5D method achieves similar quality to the fully 3D
method, but with reduced computational cost.Comment: IEEE Asilomar conference on signals systems and computers, 201
Design of Novel Loss Functions for Deep Learning in X-ray CT
Deep learning (DL) shows promise of advantages over conventional signal
processing techniques in a variety of imaging applications. The networks' being
trained from examples of data rather than explicitly designed allows them to
learn signal and noise characteristics to most effectively construct a mapping
from corrupted data to higher quality representations. In inverse problems, one
has options of applying DL in the domain of the originally captured data, in
the transformed domain of the desired final representation, or both.
X-ray computed tomography (CT), one of the most valuable tools in medical
diagnostics, is already being improved by DL methods. Whether for removal of
common quantum noise resulting from the Poisson-distributed photon counts, or
for reduction of the ill effects of metal implants on image quality,
researchers have begun employing DL widely in CT. The selection of training
data is driven quite directly by the corruption on which the focus lies.
However, the way in which differences between the target signal and measured
data is penalized in training generally follows conventional, pointwise loss
functions.
This work introduces a creative technique for favoring reconstruction
characteristics that are not well described by norms such as mean-squared or
mean-absolute error. Particularly in a field such as X-ray CT, where
radiologists' subjective preferences in image characteristics are key to
acceptance, it may be desirable to penalize differences in DL more creatively.
This penalty may be applied in the data domain, here the CT sinogram, or in the
reconstructed image. We design loss functions for both shaping and selectively
preserving frequency content of the signal
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