nils-herrmann/clean_scirepeval huggingface dataset:
Higher Degree Total Variation (HDTV) Regularization for Image Recovery

We introduce novel image regularization penalties to overcome the practical problems associated with the classical total variation (TV) scheme. Motivated by novel reinterpretations of the classical TV regularizer, we derive two families of functionals involving higher degree partial image derivatives; we term these families as isotropic and anisotropic higher degree TV (HDTV) penalties, respectively. The isotropic penalty is the mixed norm of the directional image derivatives, while the anisotropic penalty is the separable norm of directional derivatives. These functionals inherit the desirable properties of standard TV schemes such as invariance to rotations and translations, preservation of discontinuities, and convexity. The use of mixed norms in isotropic penalties encourages the joint sparsity of the directional derivatives at each pixel, thus encouraging isotropic smoothing. In contrast, the fully separable norm in the anisotropic penalty ensures the preservation of discontinuities, while continuing to smooth along the line like features; this scheme thus enhances the linenlike image characteristics analogous to standard TV. We also introduce efficient majorize-minimize algorithms to solve the resulting optimization problems. The numerical comparison of the proposed scheme with classical TV penalty, current second-degree methods, and wavelet algorithms clearly demonstrate the performance improvement. Specifically, the proposed algorithms minimize the staircase and ringing artifacts that are common with TV and wavelet schemes, while better preserving the singularities. We also observe that anisotropic HDTV penalty provides consistently improved reconstructions compared with the isotropic HDTV penalty.
Regularized inversion of noisy, incomplete MR spectroscopic imaging data with anatomical prior

This paper addresses the image reconstruction problem in MR spectroscopic imaging experiments where noisy, limited Fourier data are often collected due to temporal constraints. A parametric method is proposed which is capable of incorporating exact and uncertain boundary information. Experimental results show that the technique can generate metabolic images with much higher spatial resolution than the conventional Fourier method and existing constrained reconstruction methods
Convex clustering and recovery of partially observed data

We propose a convex clustering and reconstruction algorithm for data with missing entries. The algorithm uses a similarity measure between every pair of points to cluster and recover the data. The cluster centres can be recovered reliably when the ground-truth similarity matrix is available. Moreover, the similarity matrix can also be reliably estimated from the partially observed data, when the clusters are well-separated and the coherence of the difference between points from different clusters is low. The algorithm performs well using the estimated similarity matrix on a simulated dataset. The method is also successful in reconstructing images from under-sampled Fourier data.
Super-resolution MRI using finite rate of innovation curves

We propose a two-stage algorithm for the super-resolution of MR images from their low-frequency k-space samples. In the first stage we estimate a resolution-independent mask whose zeros represent the edges of the image. This builds off recent work extending the theory of sampling signals of finite rate of innovation (FRI) to two-dimensional curves. We enable its application to MRI by proposing extensions of the signal models allowed by FRI theory, and by developing a more robust and efficient means to determine the edge mask. In the second stage of the scheme, we recover the super-resolved MR image using the discretized edge mask as an image prior. We evaluate our scheme on simulated single-coil MR data obtained from analytical phantoms, and compare against total variation reconstructions. Our experiments show improved performance in both noiseless and noisy settings.
Multiple degree total variation (MDTV) regularization for image restoration

We introduce a novel image regularization termed as multiple degree total variation (MDTV). This type of regularization combines the first and second degree directional derivatives, thus providing a good balance between preservation of edges and region smoothness. In order to solve the resulting optimization problem, we propose an iteratively reweighted majorize minimize algorithm. We demonstrate the utility of the MDTV regularization in the context of image denoising and compressed sensing image reconstruction. We compare the proposed method with the standard TV, and the state of the art higher degree methods, including higher degree total variation (HDTV) and total generalized variation (TGV) based schemes. Numerical results indicate that the MDTV penalty provides improved image recovery performance.
Convex Recovery of Continuous Domain Piecewise Constant Images From Nonuniform Fourier Samples

We consider the recovery of a continuous domain piecewise constant image from its nonuniform Fourier samples using a convex matrix completion algorithm. We assume the discontinuities/edges of the image are localized to the zero level set of a bandlimited function. This assumption induces linear dependencies between the Fourier coefficients of the image, which results in a two-fold block Toeplitz matrix constructed from the Fourier coefficients being low rank. The proposed algorithm reformulates the recovery of the unknown Fourier coefficients as a structured low-rank matrix completion problem, where the nuclear norm of the matrix is minimized subject to structure and data constraints. We show that the exact recovery is possible with high probability when the edge set of the image satisfies an incoherency property. We also show that the incoherency property is dependent on the geometry of the edge set curve, implying higher sampling burden for smaller curves. This paper generalizes recent work on the super-resolution recovery of isolated Diracs or signals with finite rate of innovation to the recovery of piecewise constant images.
A unified energy minimization framework for nonlocal regularization

We introduce a unifying framework for the non-local regularization of biomedical inverse problems. We choose the regularization functional as the sum of distances between pairs of patches in the image. We introduce a novel majorize minimize algorithm to minimize the proposed criterion. We observe that the first iteration of the algorithm to be very similar to the classical non-local regularization schemes. In addition to providing a novel interpretation for heuristic iterative nonlocal regularization schemes, the proposed scheme enables us to develop efficient optimization algorithms, design novel non-local schemes by choosing the distance metric, and minimize local minima problems. We demonstrate the benefits of the unified framework in deblurring and compressed sensing.
NON-ITERATIVE DIXON DECOMPOSITION OF FAT ANDWATER

We introduce a non-iterative algorithm for the decomposition of fat and water from three point Dixon images. We adapt the harmonic retrieval framework to obtain the magnetic field-map as the common root of two polynomial equations; an algebraic scheme is used to solve for the field-map. To account for pixels with multiple solutions due to model mismatch, we introduce a model order estimation step. Aided by the estimate of the model order, our algorithm provides a set of feasible solutions at these pixels. We then use the prior information of the smoothness of the field-map to choose the correct solution from the set. The proposed algorithm is flexible enough to be applied to arbitrary sampling patterns and any number of metabolites. In contrast to iterative schemes that assume a single solution, the proposed scheme is not prone to ambiguous estimates due to local minima and model mismatch. This makes the algorithm applicable challenging situations, where the magnetic field is very inhomogeneous. We validate the algorithm with both phantom and human data
Image recovery using improved total variation regularization

We introduce generalized regularization functionals to overcome the practical problems associated with current total variation (TV) penalty. Specifically, we extend the TV scheme to higher order derivatives to improve the representation of smoothly varying image regions. In addition, we introduce a rotation invariant anisotropic TV penalty to improve the regularity of the edge contours. The validation of the scheme demonstrates the significantly improved performance of the proposed methods in the context of compressed sensing and denoising.
Recovery of piecewise smooth images from few fourier samples

We introduce a Prony-like method to recover a continuous domain 2-D piecewise smooth image from few of its Fourier samples. Assuming the discontinuity set of the image is localized to the zero level-set of a trigonometric polynomial, we show the Fourier transform coefficients of partial derivatives of the signal satisfy an annihilation relation. We present necessary and sufficient conditions for unique recovery of piecewise constant images using the above annihilation relation. We pose the recovery of the Fourier coefficients of the signal from the measurements as a convex matrix completion algorithm, which relies on the lifting of the Fourier data to a structured low-rank matrix; this approach jointly estimates the signal and the annihilating filter. Finally, we demonstrate our algorithm on the recovery of MRI phantoms from few low-resolution Fourier samples.
Classification of EEG with structural feature dictionaries in a brain computer interface

We present a new method for the classification of EEG in a brain computer interface by adapting subject specific features in spectral, temporal and spatial domain. For this particular purpose we extend our previous work on ECoG classification based on structural feature dictionary and apply it to extract the spectro-temporal patterns of multichannel EEG recordings related to a motor imagery task. The construction of the feature dictionary based on undecimated wavelet packet transform is extended to block FFT. We evaluate several subset selection algorithms to select a smell number of features for final classification. We tested our proposed approach on five subjects of BCI Competition 2005 dataset- IVa. By adapting the wavelet filter for each subject, the algorithm achieved an average classification accuracy of 91.4% The classification results and characteristic of selected features indicate that the proposed algorithm can jointly adapt to EEG patterns in spectm-spatio-temporal domain and provide classification accuracies as good as existing methods used in the literature.
Temporal Spectrum Sensing in Packet-Based Network Using Double Thresholds

Traditional cognitive radio protocols rely on identifying intervals of time when a channel is available to secondary users. To date, the detection of such opportunities has relied on single threshold based binary hypothesis testing schemes. In this paper, we describe the shortcomings of traditional single threshold transmit opportunity methods. We then propose a hysteresis based detection scheme that uses two different thresholds for detecting that a channel that was unavailable has become available or that an available channel is no longer available. We provide numerical simulation and experimental results to show that the proposed double threshold approach reduces interference to the primary network and enhances the throughput of the secondary network.
General chairs' welcome

On behalf of the IEEE Global Conference on Signal and Information Processing (GlobalSIP) Organizing Committee, we would like to cordially welcome you to Austin, Texas. Austin is known as the Live Music Capital of the World. Indeed, Austin offers many opportunities to experience music just a stones throw away from the convention center, but it also has much to offer residents and visitors. Austin is also the capital of Texas and is home to the University of Texas at Austin, leading technology companies, and a number of emerging startups in diverse areas from signal processing, communications, semiconductors, and gaming. Several annual events are held in Austin including the Formula 1 US Grand Prix, the Austin City Limits Music Festival (ACL), and the South-by-Southwest music, film, and interactive conference and festival (SXSW also called “Southy-By”).
Estimation of angles of arrivals of multiple wideband sources

The authors have developed a data transformation matrix for the estimation of directions of arrival for multiple wideband sources when prior information about the source locations is not available. The proposed focusing matrix minimizes the average of the squared norm of focusing error over the angles of interest without prior knowledge of source locations. This data transformation matrix does not require the preestimates which existing focusing matrices need. Using this data transformation matrix, one can remove the computational burden of preestimation. Simulation examples are used to evaluate the performance of the proposed method.<<ETX>>
Detection and modeling of acoustic emissions for fault diagnostics

The formation of microcracks in a material creates propagating ultrasonic waves that are called acoustic emissions (AEs). These AEs provide an early warning to the onset of material failure. In practical cases, however, these AEs have to be detected at very low SNRs, amongst strong interference and random noise. This paper presents some preliminary results from an ongoing investigation into the modeling and detection of AEs as a viable technique for predictive diagnostics.
Generating UWB-OFDM signal using sigma-delta modulator

In this paper we study the implementation issues of ultrawideband orthogonal frequency division multiplexing (UWB-OFDM) communication systems. Like narrowband OFDM, it is desirable to accomplish all modulation and demodulation process digitally in the base band. Designing such a transmitter and receiver for UWB-OFDM signal requires very fast and high-resolution digital-to-analog (D/A) and analog-to-digital (A/D) converters that operate on a very large frequency band.  This new structure introduces N zeros at N properly selected frequencies in the quantization noise spectrum and can be used anytime there are gaps in the spectrum of the transmitted signal. A digital transmitter and receiver for UWB-OFDM signal is proposed using this structure and its performance is studied in multipath fading channel.
Pulsed and non-pulsed OFDM ultra wideband wireless personal area networks

In this paper, we describe the leading proposal to the IEEE 802.15.3a wireless personal area networks (WPAN) standardization effort. That proposal relies on ultra wideband (UWB) orthogonal frequency division multiplexing (OFDM). We then propose an enhancement of the baseline system that uses pulsed sinusoids instead of continuous sinusoids to send information in parallel over different sub-carriers. Pulsating OFDM symbols spreads the spectrum of the modulated signals in the frequency domain leading to enhanced performance in multipath fading environments and reduced complexity and power consumption. We also provide a summary of the differences between pulsed OFDM and generalized UWB-OFDM that was proposed earlier. Finally, we present simulation results to confirm the advantages of pulsed UWB OFDM over non-pulsed wideband OFDM.
Power optimized mode selection for H.263 video coding and wireless communications

We study how to use the H.263 video communications standard efficiently to save the total consumed energy of a mobile unit in cellular networks. Particularly, we study the computational power dissipation of various operation modes available in the H.263 coding standard. We show how to achieve low power consumption in a mobile unit by judiciously selecting the operating mode of H.263 in response to the mobile environment changes (e.g., slow fading and path loss), while maintaining a good video quality level. Our preliminary results show that a mobile with the proposed method consumes 10% less energy than a mobile that comes with the most efficient power-rate mode (with a search window size of 8) of H.263 video coding in one-hour mobile communication simulation. It even saves around 32% energy over a mobile operating with an advanced coding method (using four negotiating options).
Distributed Sensor Network Localization with Inaccurate Anchor Positions and Noisy Distance Information

The goal of the sensor network localization problem is to determine positions of all the sensor nodes in a network given certain pairwise noisy distance measurements and inaccurate anchor node positions. A two-step distributed localization approach based on second-order cone programming (SOCP) relaxation is presented. In the first step, the sensor nodes determine their positions based on local information and in the second step, the anchor nodes refine their positions using information from the neighboring nodes. Our numerical study shows that the sensor and anchor positions cannot be estimated in a single step; the sensors must be estimated first for the results to converge. The second step enables anchors which are in the convex hull of their neighbors to refine their positions. Extensive simulation results with inaccurate anchor positions and noisy distance measurements are presented. These illustrate the robustness of the algorithm and the performance gains achievable in terms of problem size reduction, computational efficiency and localization accuracy.
Fast multiscale statistical signal processing algorithms

It is shown that a large set of (not necessarily stationary) correlation matrices may be transformed into a matrix that consists of essentially banded subblocks. The transformation is accomplished by premultiplication and postmultiplication with an orthogonal matrix whose elements are derived from the impulse response of a suitably designed cascade of alias-free multirate analysis filter banks. It is further proved that the Cholesky factor of the transformed matrix also consists of essentially banded subblocks. These two observations are combined to show that the linear positive definite systems of equations that arise in statistical signal processing can be solved in O(max(N log/sup 2/ (N), N/sup 2/)) operations while matrix-vector multiplication steps may be implemented in O(N log (N)) operations. An error analysis of the proposed linear positive definite system solver is also provided.<<ETX>>
Sigma filtered perceptual image coding at low bit rates

In this work we propose a novel perceptual coding strategy to encode images at low bit rates. Our approach encodes the most important visual features of an image extracted by using a sigma filter. Preliminary results show that the image decoded by our scheme contains less visual artifacts than the SPIHT decoded image at low bit rates, and is very pleasant to human eyes. Unlike other low bit rate progressive coding methods, in which the low-bit-rate decoded image quality is good only at low resolution, our scheme provides good perceptual quality regardless of resolution sizes. Our method provides a promising way to make image browsing faster while maintaining good image perceptual quality.
Attacks on quantization-based watermarking schemes

In this work, we introduce an efficient attack for removing the watermark with minimum distortion from quantization-based watermarking schemes. The attack is based on analyzing the quantization error to extract the key parameters of the encoder. The removal of the watermark is done by perturbing enough components of the quantized watermarked signal by half of the estimated quantization step. Simulation experiments show the high effectiveness of the proposed algorithm in hacking known quantization-based schemes for watermarking.
Tutorial 3: Design of Programmable Wireless Networks aka Cognitive Radios

The wide proliferation of wireless services and applications with increasing bandwidth needs is rapidly creating a spectrum shortage. However, the problem is caused primarily by inefficient legacy spectrum allocation policies, so that even when some applications suffer from lack of bandwidth, there is idle capacity in other bands. To deal with this challenge, the FCC, ITU and other regulatory organizations have begun to explore an open spectrum policy implemented by programmable wireless networks. Such wireless networks use cognitive, software reconfigurable radios to increase the efficiency of spectrum access. In particular such programmable wireless networks maximize the availability and enhance the quality of service of diverse applications using the most appropriate access network, or an aggregation of such networks, for any given local conditions. A software defined radio (SDR) terminal is essentially a reconfigurable system that can be dynamically programmed in software to reconfigure the characteristics of the hardware through the use of clearly defined APIs residing on top of a flexible hardware layer. The SDRs use different types of hardware to accomplish various communication tasks. In addition to the programmability and flexibility provided by the DSPs and software-driven communication parameters such as modulation, medium access, cryptography, etc, software defined radios also provide field service capability. So, when requirements change, code downloads, upgrades and modifications are relatively easy to execute. Ultimately, the success of the programmable wireless network vision will hinge on its ability to meet the high level needs of users, service providers, network operators and hardware and software developers.
Characteristic Phase E-Sequences in Efficient Pulse-Compression Methods using Discrete Wavelet Decomposition

We derive a family of sequences which obey a characteristic-phase constraint as it applies to finite sequences. We show these sequences are special cases of the well-known Welti sequences. We construct a transmit waveform which allows extremely efficient decomposition, whose outputs are simultaneously interpretable as adjacent correlation-filter outputs, and as lossless input signal representations. We discuss implementation of receive and transmit functions.
Geometric Invariance in image watermarking

Surviving geometric attacks in image watermarking is considered to be of great importance. In this paper, the watermark is used in an authentication context. Two solutions are being proposed for such a problem. Both geometric and invariant moments are used in the proposed techniques. An invariant watermark is designed and tested against attacks performed by StirMark using the invariant moments. On the other hand, an image normalization technique is also proposed which creates a normalized environment for watermark embedding and detection. The proposed algorithms have the advantage of being robust, computationally efficient, and no overhead needs to be transmitted to the decoder side. The proposed techniques have proven to be highly robust to all geometric manipulations, filtering, compression and slight cropping which are performed as part of StirMark attacks as well as noise addition, both Gaussian and salt & pepper.