9,227 research outputs found
Rendition: Reclaiming what a black box takes away
The premise of our work is deceptively familiar: A black box has
altered an image . Recover the image
. This black box might be any number of simple or complicated
things: a linear or non-linear filter, some app on your phone, etc. The latter
is a good canonical example for the problem we address: Given only "the app"
and an image produced by the app, find the image that was fed to the app. You
can run the given image (or any other image) through the app as many times as
you like, but you can not look inside the (code for the) app to see how it
works. At first blush, the problem sounds a lot like a standard inverse
problem, but it is not in the following sense: While we have access to the
black box and can run any image through it and observe the output,
we do not know how the block box alters the image. Therefore we have no
explicit form or model of . Nor are we necessarily interested in the
internal workings of the black box. We are simply happy to reverse its effect
on a particular image, to whatever extent possible. This is what we call the
"rendition" (rather than restoration) problem, as it does not fit the mold of
an inverse problem (blind or otherwise). We describe general conditions under
which rendition is possible, and provide a remarkably simple algorithm that
works for both contractive and expansive black box operators. The principal and
novel take-away message from our work is this surprising fact: One simple
algorithm can reliably undo a wide class of (not too violent) image
distortions.
A higher quality pdf of this paper is available at http://www.milanfar.or
Efficient Nonlinear Transforms for Lossy Image Compression
We assess the performance of two techniques in the context of nonlinear
transform coding with artificial neural networks, Sadam and GDN. Both
techniques have been successfully used in state-of-the-art image compression
methods, but their performance has not been individually assessed to this
point. Together, the techniques stabilize the training procedure of nonlinear
image transforms and increase their capacity to approximate the (unknown)
rate-distortion optimal transform functions. Besides comparing their
performance to established alternatives, we detail the implementation of both
methods and provide open-source code along with the paper.Comment: accepted as a conference contribution to Picture Coding Symposium
201
Properties of continuous Fourier extension of the discrete cosine transform and its multidimensional generalization
A versatile method is described for the practical computation of the discrete
Fourier transforms (DFT) of a continuous function given by its values
at the points of a uniform grid generated by conjugacy classes
of elements of finite adjoint order in the fundamental region of
compact semisimple Lie groups. The present implementation of the method is for
the groups SU(2), when is reduced to a one-dimensional segment, and for
in multidimensional cases. This simplest case
turns out to result in a transform known as discrete cosine transform (DCT),
which is often considered to be simply a specific type of the standard DFT.
Here we show that the DCT is very different from the standard DFT when the
properties of the continuous extensions of these two discrete transforms from
the discrete grid points to all points are
considered. (A) Unlike the continuous extension of the DFT, the continuous
extension of (the inverse) DCT, called CEDCT, closely approximates
between the grid points . (B) For increasing , the derivative of CEDCT
converges to the derivative of . And (C), for CEDCT the principle of
locality is valid. Finally, we use the continuous extension of 2-dimensional
DCT to illustrate its potential for interpolation, as well as for the data
compression of 2D images.Comment: submitted to JMP on April 3, 2003; still waiting for the referee's
Repor
Adaptive Regularization of Ill-Posed Problems: Application to Non-rigid Image Registration
We introduce an adaptive regularization approach. In contrast to conventional
Tikhonov regularization, which specifies a fixed regularization operator, we
estimate it simultaneously with parameters. From a Bayesian perspective we
estimate the prior distribution on parameters assuming that it is close to some
given model distribution. We constrain the prior distribution to be a
Gauss-Markov random field (GMRF), which allows us to solve for the prior
distribution analytically and provides a fast optimization algorithm. We apply
our approach to non-rigid image registration to estimate the spatial
transformation between two images. Our evaluation shows that the adaptive
regularization approach significantly outperforms standard variational methods
Lossy Image Compression with Compressive Autoencoders
We propose a new approach to the problem of optimizing autoencoders for lossy
image compression. New media formats, changing hardware technology, as well as
diverse requirements and content types create a need for compression algorithms
which are more flexible than existing codecs. Autoencoders have the potential
to address this need, but are difficult to optimize directly due to the
inherent non-differentiabilty of the compression loss. We here show that
minimal changes to the loss are sufficient to train deep autoencoders
competitive with JPEG 2000 and outperforming recently proposed approaches based
on RNNs. Our network is furthermore computationally efficient thanks to a
sub-pixel architecture, which makes it suitable for high-resolution images.
This is in contrast to previous work on autoencoders for compression using
coarser approximations, shallower architectures, computationally expensive
methods, or focusing on small images
Multispectral Palmprint Recognition Using a Hybrid Feature
Personal identification problem has been a major field of research in recent
years. Biometrics-based technologies that exploit fingerprints, iris, face,
voice and palmprints, have been in the center of attention to solve this
problem. Palmprints can be used instead of fingerprints that have been of the
earliest of these biometrics technologies. A palm is covered with the same skin
as the fingertips but has a larger surface, giving us more information than the
fingertips. The major features of the palm are palm-lines, including principal
lines, wrinkles and ridges. Using these lines is one of the most popular
approaches towards solving the palmprint recognition problem. Another robust
feature is the wavelet energy of palms. In this paper we used a hybrid feature
which combines both of these features. %Moreover, multispectral analysis is
applied to improve the performance of the system. At the end, minimum distance
classifier is used to match test images with one of the training samples. The
proposed algorithm has been tested on a well-known multispectral palmprint
dataset and achieved an average accuracy of 98.8\%.Comment: 6 page
Signal and Image Processing with Sinlets
This paper presents a new family of localized orthonormal bases - sinlets -
which are well suited for both signal and image processing and analysis.
One-dimensional sinlets are related to specific solutions of the time-dependent
harmonic oscillator equation. By construction, each sinlet is infinitely
differentiable and has a well-defined and smooth instantaneous frequency known
in analytical form. For square-integrable transient signals with infinite
support, one-dimensional sinlet basis provides an advantageous alternative to
the Fourier transform by rendering accurate signal representation via a
countable set of real-valued coefficients. The properties of sinlets make them
suitable for analyzing many real-world signals whose frequency content changes
with time including radar and sonar waveforms, music, speech, biological
echolocation sounds, biomedical signals, seismic acoustic waves, and signals
employed in wireless communication systems. One-dimensional sinlet bases can be
used to construct two- and higher-dimensional bases with variety of potential
applications including image analysis and representation.Comment: 26 pages, 21 figure
Taylor Series as Wide-sense Biorthogonal Wavelet Decomposition
Pointwise-supported generalized wavelets are introduced, based on Dirac,
doublet and further derivatives of delta. A generalized biorthogonal analysis
leads to standard Taylor series and new Dual-Taylor series that may be
interpreted as Laurent Schwartz distributions. A Parseval-like identity is also
derived for Taylor series, showing that Taylor series support an energy
theorem. New representations for signals called derivagrams are introduced,
which are similar to spectrograms. This approach corroborates the impact of
wavelets in modern signal analysis.Comment: 6 pages, 4 figures. conference: XXII Simposio Brasileiro de
Telecomunicacoes, SBrT'05, 2005, Campinas, SP, Brazi
Quality Adaptive Low-Rank Based JPEG Decoding with Applications
Small compression noises, despite being transparent to human eyes, can
adversely affect the results of many image restoration processes, if left
unaccounted for. Especially, compression noises are highly detrimental to
inverse operators of high-boosting (sharpening) nature, such as deblurring and
superresolution against a convolution kernel. By incorporating the non-linear
DCT quantization mechanism into the formulation for image restoration, we
propose a new sparsity-based convex programming approach for joint compression
noise removal and image restoration. Experimental results demonstrate
significant performance gains of the new approach over existing image
restoration methods
Covariance Eigenvector Sparsity for Compression and Denoising
Sparsity in the eigenvectors of signal covariance matrices is exploited in
this paper for compression and denoising. Dimensionality reduction (DR) and
quantization modules present in many practical compression schemes such as
transform codecs, are designed to capitalize on this form of sparsity and
achieve improved reconstruction performance compared to existing
sparsity-agnostic codecs. Using training data that may be noisy a novel
sparsity-aware linear DR scheme is developed to fully exploit sparsity in the
covariance eigenvectors and form noise-resilient estimates of the principal
covariance eigenbasis. Sparsity is effected via norm-one regularization, and
the associated minimization problems are solved using computationally efficient
coordinate descent iterations. The resulting eigenspace estimator is shown
capable of identifying a subset of the unknown support of the eigenspace basis
vectors even when the observation noise covariance matrix is unknown, as long
as the noise power is sufficiently low. It is proved that the sparsity-aware
estimator is asymptotically normal, and the probability to correctly identify
the signal subspace basis support approaches one, as the number of training
data grows large. Simulations using synthetic data and images, corroborate that
the proposed algorithms achieve improved reconstruction quality relative to
alternatives.Comment: IEEE Transcations on Signal Processing, 2012 (to appear
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