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SeDDaRA
Abstracts
J.N. Caron,
"Application of SeDDaRA Blind Deconvolution for Efficient Improvement
of Confocal Microscopy Images", Quarktet Technical Note, February 2,
2011.
This paper presents the application of the SeDDaRA blind deconvolution
method to image sequences produced by confocal microscopy. In
contrast to more common methods, such as Lucy-Richardson and maximum
entropy, SeDDaRA is non-iterative. As such, this approach
requires significantly less computation while still producing
significant blur reduction. Based on scene-statistics,
successful application of this method results from finding a suitable
representation of the scene, as opposed to deriving a reasonable model
of the point spread function. The method is described and
applied to two distinct confocal image stacks that are available from
the National Institute of Health web site. A
resolution-based image metric, derived from the full-width half-maximum
values of image features, is applied to assess image improvement.
Based on visual inspection and the image metric, the processed
image sets show significant improvements in image resolution and
contrast.
W.E. Vanderlinde and J.N. Caron, "Blind
Deconvolution of SEM Images," Proceedings
of
the 33rd International Symposium for Testing and Failure Analysis,
November,
2007, p. 97-102.
Blind deconvolution techniques were used to enhance scanning electron
microscope (SEM) images in the range of 200,000x to 500,000x
magnification. Typical SEM samples were imaged including a gold
island reference standard, a plams delayered integrated circuit, and an
integrated circuit cross section. Image resolution improvement up
to 40% was observed. However, it was necessary to use 16-bit
images with great than 120:1 signal to noise ratio, which required 10
minute frame times.
J.N. Caron,
"Blind
deconvolution
of
audio-frequency signals using the self-deconvolving data restoration
algorithm," Journal
of
the Acoustical
Society of America, Vol. 116, Issue 1,
pp. 373-378, 2004.
A signal
processing algorithm has been developed in which a filter function is
extracted from degraded data through mathematical operations. The
filter function can be used to restore much of the degraded content of
the data through use of a deconvolution process. The operation can be
performed without prior knowledge of the detection system, a technique
known as blind deconvolution. The extraction process, designated
Self-deconvolving Data Reconstruction Algorithm (SeDDaRA), is applied
here to audio-frequency signals showing significant qualitative
improvement. Degradation arising from the process of electronic
recording and reproduction is significantly reduced.
J.N.
Caron, N.M. Namazi, and
C.J. Rollins,
"Noniterative blind data restoration by use of an extracted filter
function," Applied Optics,
November 10, 2002, Vol 41, No. 32, p. 6884.
A signal-processing algorithm has been developed where a filter
function is extracted from degraded data through mathematical
operations. The filter function can then be used to restore much of the
degraded content of the data through use of a deconvolution algorithm.
This process can be performed without prior knowledge of the detection
system, a technique known as blind deconvolution. The extraction
process, designated self-deconvolving data reconstruction algorithm,
has been used successfully to restore digitized photographs, digitized
acoustic waveforms, and other forms of data. The process is
non-iterative, computationally efficient, and requires little user
input. Implementation is straightforward, allowing inclusion into many
types of signal-processing software and hardware. The novelty of the
invention is the application of a power law and smoothing function to
the degraded data in frequency space. Two methods for determining the
value of the power law are discussed. The first method assumes the
power law is frequency dependent. The function derived comparing the
frequency spectrum of the degraded data with the spectrum of a signal
with the desired frequency response. The second method assumes this
function is a constant of frequency. This approach requires little
knowledge of the original data or the degradation.
J.N. Caron, "Rapid
supersampling of multiframe sequences by use of blind
deconvolution," Optics Letters, September, 2004, Vol 29,
No. 17, p.
1986-1988.
Under
certain
conditions, multi-frame image sequences can be processed to
produce images that achieve greater resolution through image
registration and increased sampling. This technique, known as
super-sampling, takes advantage of the spatial-temporal data available
in an under-sampled imaging sequence. In this effort, the image
registration is replaced by application of a fast blind deconvolution
technique to remove the motion blur in the up-sampled average of the
image sequence. This produces a super-sampled image with significantly
decreased computational requirements compared to common methods. Method
and simulated test results are presented.
J.N. Caron,
N.M. Namazi, R.L. Lucke, C.J. Rollins, and P.R. Lynn, Jr., "Blind data
restoration with an extracted filter function," Optics Letters
,
August 1, 2001, Vol 26, No.15, p. 1164.
A method for performing blind deconvolutions on degraded images and
data has been developed. The technique uses a power law relation
applied to the Fourier transform of the degraded data to extract a
filter function. This filter function closely resembles the point
spread function of the system, and can be used to restore and enhance
higher-frequency content. The process is non-iterative and requires
only that the point spread function be space-invariant and the transfer
function is real. The algorithm has been validated by direct
comparisons using a pseudo-inverse filter with known transfer functions.
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