5,781 research outputs found
Convolutional Deblurring for Natural Imaging
In this paper, we propose a novel design of image deblurring in the form of
one-shot convolution filtering that can directly convolve with naturally
blurred images for restoration. The problem of optical blurring is a common
disadvantage to many imaging applications that suffer from optical
imperfections. Despite numerous deconvolution methods that blindly estimate
blurring in either inclusive or exclusive forms, they are practically
challenging due to high computational cost and low image reconstruction
quality. Both conditions of high accuracy and high speed are prerequisites for
high-throughput imaging platforms in digital archiving. In such platforms,
deblurring is required after image acquisition before being stored, previewed,
or processed for high-level interpretation. Therefore, on-the-fly correction of
such images is important to avoid possible time delays, mitigate computational
expenses, and increase image perception quality. We bridge this gap by
synthesizing a deconvolution kernel as a linear combination of Finite Impulse
Response (FIR) even-derivative filters that can be directly convolved with
blurry input images to boost the frequency fall-off of the Point Spread
Function (PSF) associated with the optical blur. We employ a Gaussian low-pass
filter to decouple the image denoising problem for image edge deblurring.
Furthermore, we propose a blind approach to estimate the PSF statistics for two
Gaussian and Laplacian models that are common in many imaging pipelines.
Thorough experiments are designed to test and validate the efficiency of the
proposed method using 2054 naturally blurred images across six imaging
applications and seven state-of-the-art deconvolution methods.Comment: 15 pages, for publication in IEEE Transaction Image Processin
Design and FPGA Implementation of Variable FIR Filters using the Spectral Parameter Approximation and Time-Domain Approach
This brief present a design and FPGA implementation of variable FIR filters using time domain approach of the spectral parameter approximation (SPA) technique. Farrow structure is used to implement the SPA-based filter. In the design of variable filters first design the practical filters which satisfy the given transition bandwidth, passband ripple, and stopband attenuation specifications and then approximate the coefficients of these filters by the impulse response of the Farrow structure. Least-squares technique is used to approximation problem. Various design and implementation cases with FPGA synthesis results are presented
On the eigenfilter design method and its applications: a tutorial
The eigenfilter method for digital filter design involves the computation of filter coefficients as the eigenvector of an appropriate Hermitian matrix. Because of its low complexity as compared to other methods as well as its ability to incorporate various time and frequency-domain constraints easily, the eigenfilter method has been found to be very useful. In this paper, we present a review of the eigenfilter design method for a wide variety of filters, including linear-phase finite impulse response (FIR) filters, nonlinear-phase FIR filters, all-pass infinite impulse response (IIR) filters, arbitrary response IIR filters, and multidimensional filters. Also, we focus on applications of the eigenfilter method in multistage filter design, spectral/spacial beamforming, and in the design of channel-shortening equalizers for communications applications
Design and analysis of a control system for an optical delay-line circuit used as reconfigurable gain equalizer
The design and analysis of a control system for a coherent two-port lattice-form optical delay-line circuit used as reconfigurable gain equalizer is presented. The design of the control system, which is based on a real device model and a least-square optimization method, is described in detail. Analysis on a five-stage device for the 32 possible solutions of phase parameters showed that, for some filter characteristics, the variations in power dissipation can vary up to a factor of 2. Furthermore, the solution selection has influence on the optimization result and number of iterations needed. A sensitivity analysis of the phase parameters showed that the allowable error in the phase parameters should not exceed a standard deviation of /spl pi//500 in order to achieve a total maximal absolute accuracy error not greater than approximately 0.6 dB. A five-stage device has been fabricated using planar lightwave circuit technology that uses the thermooptic effect. Excellent agreement between simulations and measurements has been achieved
Analysis and application of digital spectral warping in analog and mixed-signal testing
Spectral warping is a digital signal processing transform which shifts the frequencies contained within a signal along the frequency axis. The Fourier transform coefficients of a warped signal correspond to frequency-domain 'samples' of the original signal which are unevenly spaced along the frequency axis. This property allows the technique to be efficiently used for DSP-based analog and mixed-signal testing. The analysis and application of spectral warping for test signal generation, response analysis, filter design, frequency response evaluation, etc. are discussed in this paper along with examples of the software and hardware implementation
Oversampling PCM techniques and optimum noise shapers for quantizing a class of nonbandlimited signals
We consider the efficient quantization of a class of nonbandlimited signals, namely, the class of discrete-time signals that can be recovered from their decimated version. The signals are modeled as the output of a single FIR interpolation filter (single band model) or, more generally, as the sum of the outputs of L FIR interpolation filters (multiband model). These nonbandlimited signals are oversampled, and it is therefore reasonable to expect that we can reap the same benefits of well-known efficient A/D techniques that apply only to bandlimited signals. We first show that we can obtain a great reduction in the quantization noise variance due to the oversampled nature of the signals. We can achieve a substantial decrease in bit rate by appropriately decimating the signals and then quantizing them. To further increase the effective quantizer resolution, noise shaping is introduced by optimizing prefilters and postfilters around the quantizer. We start with a scalar time-invariant quantizer and study two important cases of linear time invariant (LTI) filters, namely, the case where the postfilter is the inverse of the prefilter and the more general case where the postfilter is independent from the prefilter. Closed form expressions for the optimum filters and average minimum mean square error are derived in each case for both the single band and multiband models. The class of noise shaping filters and quantizers is then enlarged to include linear periodically time varying (LPTV)M filters and periodically time-varying quantizers of period M. We study two special cases in great detail
Towards low-latency real-time detection of gravitational waves from compact binary coalescences in the era of advanced detectors
Electromagnetic (EM) follow-up observations of gravitational wave (GW) events
will help shed light on the nature of the sources, and more can be learned if
the EM follow-ups can start as soon as the GW event becomes observable. In this
paper, we propose a computationally efficient time-domain algorithm capable of
detecting gravitational waves (GWs) from coalescing binaries of compact objects
with nearly zero time delay. In case when the signal is strong enough, our
algorithm also has the flexibility to trigger EM observation before the merger.
The key to the efficiency of our algorithm arises from the use of chains of
so-called Infinite Impulse Response (IIR) filters, which filter time-series
data recursively. Computational cost is further reduced by a template
interpolation technique that requires filtering to be done only for a much
coarser template bank than otherwise required to sufficiently recover optimal
signal-to-noise ratio. Towards future detectors with sensitivity extending to
lower frequencies, our algorithm's computational cost is shown to increase
rather insignificantly compared to the conventional time-domain correlation
method. Moreover, at latencies of less than hundreds to thousands of seconds,
this method is expected to be computationally more efficient than the
straightforward frequency-domain method.Comment: 19 pages, 6 figures, for PR
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