1,396 research outputs found
"Rewiring" Filterbanks for Local Fourier Analysis: Theory and Practice
This article describes a series of new results outlining equivalences between
certain "rewirings" of filterbank system block diagrams, and the corresponding
actions of convolution, modulation, and downsampling operators. This gives rise
to a general framework of reverse-order and convolution subband structures in
filterbank transforms, which we show to be well suited to the analysis of
filterbank coefficients arising from subsampled or multiplexed signals. These
results thus provide a means to understand time-localized aliasing and
modulation properties of such signals and their subband
representations--notions that are notably absent from the global viewpoint
afforded by Fourier analysis. The utility of filterbank rewirings is
demonstrated by the closed-form analysis of signals subject to degradations
such as missing data, spatially or temporally multiplexed data acquisition, or
signal-dependent noise, such as are often encountered in practical signal
processing applications
Multi-Modal Enhancement Techniques for Visibility Improvement of Digital Images
Image enhancement techniques for visibility improvement of 8-bit color digital images based on spatial domain, wavelet transform domain, and multiple image fusion approaches are investigated in this dissertation research.
In the category of spatial domain approach, two enhancement algorithms are developed to deal with problems associated with images captured from scenes with high dynamic ranges. The first technique is based on an illuminance-reflectance (I-R) model of the scene irradiance. The dynamic range compression of the input image is achieved by a nonlinear transformation of the estimated illuminance based on a windowed inverse sigmoid transfer function. A single-scale neighborhood dependent contrast enhancement process is proposed to enhance the high frequency components of the illuminance, which compensates for the contrast degradation of the mid-tone frequency components caused by dynamic range compression. The intensity image obtained by integrating the enhanced illuminance and the extracted reflectance is then converted to a RGB color image through linear color restoration utilizing the color components of the original image. The second technique, named AINDANE, is a two step approach comprised of adaptive luminance enhancement and adaptive contrast enhancement. An image dependent nonlinear transfer function is designed for dynamic range compression and a multiscale image dependent neighborhood approach is developed for contrast enhancement. Real time processing of video streams is realized with the I-R model based technique due to its high speed processing capability while AINDANE produces higher quality enhanced images due to its multi-scale contrast enhancement property. Both the algorithms exhibit balanced luminance, contrast enhancement, higher robustness, and better color consistency when compared with conventional techniques.
In the transform domain approach, wavelet transform based image denoising and contrast enhancement algorithms are developed. The denoising is treated as a maximum a posteriori (MAP) estimator problem; a Bivariate probability density function model is introduced to explore the interlevel dependency among the wavelet coefficients. In addition, an approximate solution to the MAP estimation problem is proposed to avoid the use of complex iterative computations to find a numerical solution. This relatively low complexity image denoising algorithm implemented with dual-tree complex wavelet transform (DT-CWT) produces high quality denoised images
Digital encoding of black and white facsimile signals
As the costs of digital signal processing and memory hardware are
decreasing each year compared to those of transmission, it is
increasingly economical to apply sophisticated source encoding
techniques to reduce the transmission time for facsimile documents.
With this intent, information lossy encoding schemes have been
investigated in which the encoder is divided into two stages.
Firstly, preprocessing, which removes redundant information from
the original documents, and secondly, actual encoding of the preprocessed
documents. [Continues.
Methodological considerations of integrating portable digital technologies in the analysis and management of complex superimposed Californian pictographs: From spectroscopy and spectral imaging to 3-D scanning
How can the utilization of newly developed advanced portable technologies give us greater understandings of the most complex of prehistoric rock art? This is the questions driving The Gordian Knot project analysing the polychrome Californian site known as Pleito. New small transportable devices allow detailed on-site analyses of rock art. These non-destructive portable technologies can use X-ray and Raman technology to determine the chemical elements used to make the pigment that makes the painting; they can use imaging techniques such as Highlight Reflective Transformation Imaging and dStretch© to enhance their visibility; they can use digital imagery to disentangle complex superimposed paintings; and they can use portable laser instruments to analyse the micro-topography of the rock surface and integrate these technologies into a 3-D environment. This paper outlines a robust methodology and preliminary results to show how an integration of different portable technologies can serve rock art research and management
JSSL: Joint Supervised and Self-supervised Learning for MRI Reconstruction
Magnetic Resonance Imaging represents an important diagnostic modality;
however, its inherently slow acquisition process poses challenges in obtaining
fully sampled k-space data under motion in clinical scenarios such as
abdominal, cardiac, and prostate imaging. In the absence of fully sampled
acquisitions, which can serve as ground truth data, training deep learning
algorithms in a supervised manner to predict the underlying ground truth image
becomes an impossible task. To address this limitation, self-supervised methods
have emerged as a viable alternative, leveraging available subsampled k-space
data to train deep learning networks for MRI reconstruction. Nevertheless,
these self-supervised approaches often fall short when compared to supervised
methodologies. In this paper, we introduce JSSL (Joint Supervised and
Self-supervised Learning), a novel training approach for deep learning-based
MRI reconstruction algorithms aimed at enhancing reconstruction quality in
scenarios where target dataset(s) containing fully sampled k-space measurements
are unavailable. Our proposed method operates by simultaneously training a
model in a self-supervised learning setting, using subsampled data from the
target dataset(s), and in a supervised learning manner, utilizing data from
other datasets, referred to as proxy datasets, where fully sampled k-space data
is accessible. To demonstrate the efficacy of JSSL, we utilized subsampled
prostate parallel MRI measurements as the target dataset, while employing fully
sampled brain and knee k-space acquisitions as proxy datasets. Our results
showcase a substantial improvement over conventional self-supervised training
methods, thereby underscoring the effectiveness of our joint approach. We
provide a theoretical motivation for JSSL and establish a practical
"rule-of-thumb" for selecting the most appropriate training approach for deep
MRI reconstruction.Comment: 26 pages, 11 figures, 6 table
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