nils-herrmann/clean_scirepeval huggingface dataset:
VITRAIL: Acquisition, Modeling, and Rendering of Stained Glass

Stained glass windows are designed to reveal their powerful artistry under diverse and time-varying lighting conditions; virtual relighting of stained glass, therefore, represents an exceptional tool for the appreciation of this age old art form. However, as opposed to most other artifacts, stained glass windows are extremely difficult if not impossible to analyze using controlled illumination because of their size and position. In this paper, we present novel methods built upon image based priors to perform virtual relighting of stained glass artwork by acquiring the actual light transport properties of a given artifact. In a preprocessing step, we build a material-dependent dictionary for light transport by studying the scattering properties of glass samples in a laboratory setup. We can now use the dictionary to recover a light transport matrix in two ways: under controlled illuminations the dictionary constitutes a sparsifying basis for a compressive sensing acquisition, while in the case of uncontrolled illuminations the dictionary is used to perform sparse regularization. The proposed basis preserves volume impurities and we show that the retrieved light transport matrix is heterogeneous, as in the case of real world objects. We present the rendering results of several stained glass artifacts, including the Rose Window of the Cathedral of Lausanne, digitized using the presented methods.
Manipulating rates, complexity and error-resilience with discrete transforms

The common conception of transform coding is a a computationally efficient alternative to vector quantization. At high rates, it is not the partitioning itself but the efficiency of the scalar entropy coding which makes transform coding useful. With this view a class of discrete transforms derived from linear transforms are used to pursue three objectives: reducing coefficient entropies (getting coding gain as in conventional transform coding), reducing the complexity of entropy coding (allowing many coefficients to be efficiently coded with identical entropy codes), and having robustness to coefficient erasures.
Network correlated data gathering with explicit communication: NP-completeness and algorithms

We consider the problem of correlated data gathering by a network with a sink node and a tree-based communication structure, where the goal is to minimize the total transmission cost of transporting the information collected by the nodes, to the sink node. For source coding of correlated data, we consider a joint entropy-based coding model with explicit communication where coding is simple and the transmission structure optimization is difficult. We first formulate the optimization problem definition in the general case and then we study further a network setting where the entropy conditioning at nodes does not depend on the amount of side information, but only on its availability. We prove that even in this simple case, the optimization problem is NP-hard. We propose some efficient, scalable, and distributed heuristic approximation algorithms for solving this problem and show by numerical simulations that the total transmission cost can be significantly improved over direct transmission or the shortest path tree. We also present an approximation algorithm that provides a tree transmission structure with total cost within a constant factor from the optimal.
Sampling at unknown locations, with an application in surface retrieval

We consider the problem of sampling at unknown locations. We prove that, in this setting, if we take arbitrarily many samples of a polynomial or real bandlimited signal, it is possible to find another function in the same class, arbitrarily far away from the original, that could have generated the same samples. In other words, the error can be arbitrarily large. Motivated by this, we prove that, for polynomials, if the sample positions are constrained such that they can be described by an unknown rational function, uniqueness can be achieved. In addition to our theoretical results, we show that, in 1-D, the problem of recovering a painted surface from a single image exactly fits this framework. Furthermore, we propose a simple iterative algorithm for recovering both the surface and the texture and test it with simple simulations.
Best wavelet packet bases in a rate-distortion sense

A fast rate-distortion (R-D) optimal scheme for coding adaptive trees whose individual nodes spawn descendents forming a disjoint and complete basis cover for the space spanned by their parent nodes is presented. The scheme guarantees operation on the convex hull of the operational R-D curve and uses a fast dynamic programing pruning algorithm to markedly reduce computational complexity. Applications for this coding technique include R. Coefman et al.'s ( 1990) generalized multiresolution wavelet packet decomposition, iterative subband coders, and quadtree structures. Applications to image processing involving wavelet packets as well as discrete cosine transform (DCT) quadtrees are presented.
Experiences with Reproducible Research in Various Facets of Signal Processing Research

How often have you been able to implement an algorithm as it is described in a paper? And when you did, were you confident that you had exactly the same parameter values and results as the authors of the paper? All too often, articles do not describe all the details of an algorithm and thus prohibit an implementation by someone else. In this paper, we describe our experience with reproducible research, a paradigm to allow other people to reproduce with minimal effort the results we have obtained. We discuss both the reproducibility of data and algorithms, and give examples for each of them. The effort required to make research reproducible is compensated by a higher visibility and impact of the results.
Single-channel indoor microphone localization

We propose a novel method for single-channel microphone localization inside a known room. Unlike other approaches, we take advantage of the room reverberation, which enables us to use only a single fixed loudspeaker to localize the microphone. Our method uses an echo labeling approach that associates the echoes to the correct walls. Echo labeling leverages the properties of the Euclidean distance matrices formed from the distances between the virtual sources and the microphone. Experiments performed in a real lecture room verify the effectiveness of the proposed localization algorithm.
Sampling and reconstructing spatial fields using mobile sensors

The classical approach to sampling time-invariant spatial fields uses static sensors distributed over space. We study a new approach involving mobile sensors that move through space measuring the field values along their paths. A single moving sensor can take measurements over a wide spatial area thus acting as a substitute for a potentially large number of static sensors. A moving sensor encounters the spatial field in its path in the form of a time-domain signal. Hence a time-domain anti-aliasing filter can be employed at the mobile sensor to limit the amount of out-of-band noise prior to sampling. We analytically quantify the advantage of mobile sensing over static sensing in rejecting out-of-band noise. We also demonstrate via simulations the improvement in reconstruction accuracy that can be obtained using mobile sensors and filtering in a temperature measurement problem.
The Distributed Multiple Voting Problem

A networked set of agents holding binary opinions does not seem to be able to compute its majority opinion by means of local binary interactions only. However, the majority problem can be solved using two or more bits, instead of one (F. Bénézit , “Interval consensus: From quantized gossip to voting”, Apr. 2009, 3664). Pairs of agents asynchronously exchange their states and update them according to a voting automaton. This paper presents binary voting automata as well as solutions to the multiple voting problem, where agents can vote for one candidate among |C| ≥ 2 candidates and need to determine the majority vote. The voting automata are derived from the pairwise gossip algorithm, which computes averages. In the binary case (|C|=2), we focus on averages in dimension 1, but in the multiple case (|C| ≥ 2) we quantize gossip in dimension |C| - 1 , which is larger than or equal to 1. We show in particular that a consensus on majority can be reached using 15 possible states (4 bits) for the ternary voting problem, and using 100 possible states (7 bits) for the quaternary voting problem.
The finite ridgelet transform for image representation

The ridgelet transform was introduced as a sparse expansion for functions on continuous spaces that are smooth away from discontinuities along lines. We propose an orthonormal version of the ridgelet transform for discrete and finite-size images. Our construction uses the finite Radon transform (FRAT) as a building block. To overcome the periodization effect of a finite transform, we introduce a novel ordering of the FRAT coefficients. We also analyze the FRAT as a frame operator and derive the exact frame bounds. The resulting finite ridgelet transform (FRIT) is invertible, nonredundant and computed via fast algorithms. Furthermore, this construction leads to a family of directional and orthonormal bases for images. Numerical results show that the FRIT is more effective than the wavelet transform in approximating and denoising images with straight edges.
Adaptive wavelet thresholding for image denoising and compression

The first part of this paper proposes an adaptive, data-driven threshold for image denoising via wavelet soft-thresholding. The threshold is derived in a Bayesian framework, and the prior used on the wavelet coefficients is the generalized Gaussian distribution (GGD) widely used in image processing applications. The proposed threshold is simple and closed-form, and it is adaptive to each subband because it depends on data-driven estimates of the parameters. Experimental results show that the proposed method, called BayesShrink, is typically within 5% of the MSE of the best soft-thresholding benchmark with the image assumed known. It also outperforms SureShrink (Donoho and Johnstone 1994, 1995; Donoho 1995) most of the time. The second part of the paper attempts to further validate claims that lossy compression can be used for denoising. The BayesShrink threshold can aid in the parameter selection of a coder designed with the intention of denoising, and thus achieving simultaneous denoising and compression. Specifically, the zero-zone in the quantization step of compression is analogous to the threshold value in the thresholding function. The remaining coder design parameters are chosen based on a criterion derived from Rissanen's minimum description length (MDL) principle. Experiments show that this compression method does indeed remove noise significantly, especially for large noise power. However, it introduces quantization noise and should be used only if bitrate were an additional concern to denoising.
Quantized Overcomplete Expansions in IRN: Analysis, Synthesis, and Algorithms

Coefficient quantization has peculiar qualitative effects on representations of vectors in IR with respect to overcomplete sets of vectors. These effects are investigated in two settings: frame expansions (representations obtained by forming inner products with each element of the set) and matching pursuit expansions (approximations obtained by greedily forming linear combinations). In both cases, based on the concept of consistency, it is shown that traditional linear reconstruction methods are suboptimal, and better consistent reconstruction algorithms are given. The proposed consistent reconstruction algorithms were in each case implemented, and experimental results are included. For frame expansions, results are proven to bound distortion as a function of frame redundancy r and quantization step size for linear, consistent, and optimal reconstruction methods. Taken together, these suggest that optimal reconstruction methods will yield O(1/r/sup 2/) mean-squared error (MSE), and that consistency is sufficient to insure this asymptotic behavior. A result on the asymptotic tightness of random frames is also proven. Applicability of quantized matching pursuit to lossy vector compression is explored. Experiments demonstrate the likelihood that a linear reconstruction is inconsistent, the MSE reduction obtained with a nonlinear (consistent) reconstruction algorithm, and generally competitive performance at low bit rates.
Oversampled FIR filter banks and frames in l/sup 2/(Z)

Perfect reconstruction FIR filter banks are equivalent to a particular class of frames in l/sup 2/(Z). These frames are the subject of this paper. Necessary and sufficient conditions on a filter bank to implement a frame or a tight frame decomposition are given, as well as the necessary and sufficient condition for perfect reconstruction using FIR filters. Complete parameterizations of FIR filter banks satisfying these conditions are also given. Further, we study the condition under which the frame dual to the frame associated to an FIR filter bank is also FIR, and give a parameterization of a class of filter banks having this property.
Fast and Robust Parametric Estimation of Jointly Sparse Channels

We consider the joint estimation of multipath channels obtained with a set of receiving antennas and uniformly probed in the frequency domain. This scenario fits most of the modern outdoor communication protocols for mobile access (ETSI Std. 125 913) or digital broadcasting (ETSI Std. 300 744) among others. Such channels verify a sparse common support (SCS) property which was used in the work of Barbotin et al. (2012) to propose a finite rate of innovation (FRI)-based sampling and estimation algorithm. In this paper, we improve the robustness and computational complexity aspects of this algorithm. The method is based on projection in Krylov subspaces to improve complexity and a new criterion called the partial effective rank (PER) to estimate the level of sparsity to gain robustness. If P antennas measure a K-multipath channel with N uniformly sampled measurements per channel, the algorithm possesses an O(KPNlogN) complexity and an O(KPN)memory footprint instead of O(PN3) and O(PN2) for the direct implementation, making it suitable for K≪N. The sparsity is estimated online based on the PER, and the algorithm therefore has a sense of introspection being able to relinquish sparsity if it is lacking. The estimation performances are tested on field measurements with synthetic additive white Gaussian noise, and the proposed algorithm outperforms nonsparse reconstruction in the medium to low signal-to-noise ratio range (≤ 0 dB), increasing the rate of successful symbol decoding by 1/10 in average, and 1/3 in the best case. The experiments also show that the algorithm does not perform worse than a nonsparse estimation algorithm in nonsparse operating conditions, since it may fall-back to it if the PER criterion does not detect a sufficient level of sparsity. The algorithm is also tested against a method assuming a “discrete” sparsity model as in compressed sensing. The conducted test indicates a trade-off between speed and accuracy.
Irregular sampling with unknown locations

This paper is concerned with finding the locations of an irregularly sampled finite discrete-time band-limited signal. First a geometrical approach is described and is transformed into an optimization problem. Due to the structure of the problem, multiple solutions exist and are shifts of each other. Three methods of solution are suggested: an exhaustive method which finds the exact set of locations; random search method and cyclic coordinate method, both descent methods, which find approximate or exact solutions. The cyclic coordinate method is less likely to fall in a local minimum and proves to be more satisfactory than the random search method in the presence of jitter. A practical example, where a signal is sampled several times with a regular spacing, is also described.
Beyond Moore-Penrose: Sparse pseudoinverse

Frequently, we use the Moore-Penrose pseudoinverse (MPP) even in cases when we do not require all of its defining properties. But if the running time and the storage size are critical, we can do better. By discarding some constraints needed for the MPP, we gain freedom to optimize other aspects of the new pseudoinverse. A sparser pseudoinverse reduces the amount of computation and storage. We propose a method to compute a sparse pseudoinverse and show that it offers sizable improvements in speed and storage, with a small loss in the least-squares performance. Differently from previous approaches, we do not attempt to approximate the MPP, but rather to produce an exact but sparse pseudoinverse. In the underdetermined (compressed sensing) scenario we prove that the rescaled sparse pseudoinverse yields an unbiased estimate of the unknown vector, and we demonstrate its potential in iterative sparse recovery algorithms, pointing out directions for future research.
Joint source/channel coding for multicast packet video

Current schemes for multicast packet video cope with congestion by adapting the transmission rate of a variable rate codec at the source. We propose a new approach, based on receiver- rather than source-adaptation, where a video source is encoded hierarchically with each layer of hierarchy distributed on a separate network channel. Each receiver can then dynamically adapt to local network capacity by adjusting the number of layers it receives. In order to deploy such a system, we must at the same time develop a layered codec tailored for this model. We present a prototype coder that has been designed specifically for our receiver-based congestion avoidance scheme. In order to evaluate the efficacy of our approach, we have implemented it in an existing Internet remote conferencing application and constrained the complexity of our design to run in real-time on standard workstations. Even with this constraint, our codec can generate a flexible range of layers while exhibiting reasonable compression performance.
Distributed Spatial Audio Coding in Wireless Hearing Aids

The information content of binaural signals can be beneficial to many algorithms deployed in current digital hearing aids. However, the exchange of such signals over a wireless communication link requires transmission schemes that must fulfill demanding technical constraints. We present a distributed coding algorithm that builds on psychoacoustic principles in order to achieve this goal with low bitrates, while preserving affordable complexity. The key steps of the proposed algorithm are detailed and the accuracy of the signal exchange mechanism is evaluated in simple simulated acoustic scenarios.
Frame reconstruction of the Laplacian pyramid

We study the Laplacian pyramid (LP) as a frame operator, and this reveals that the usual reconstruction is suboptimal. With orthogonal filters, the LP is shown to be a tight frame, thus the optimal linear reconstruction using the dual frame operator has a simple structure as symmetrical with the forward transform. For more general cases, we propose an efficient filter bank for reconstruction in the LP that is shown to perform better than the usual method. Numerical results indicate that gains of more than 1 dB are actually achieved.
Sampling trajectories for mobile sensing

Classical sampling theory for sampling and reconstructing bandlimited fields in Rd addresses the problem of sampling on lattice points. We consider a generalization of this problem, in which one samples the field along 1-dimensional spatial trajectories in Rd rather than at points. The process of sampling records the value of the field at all points on the sampling trajectories. Such a sampling setup is relevant in the problem of spatial sampling using mobile sensors. We study various possible designs of sampling trajectories and discuss necessary and sufficient conditions for perfect reconstruction. We introduce a density metric for trajectories which we call path density, that quantifies the total length of these trajectories per unit volume in Rd. We formulate the problem of identifying optimal sampling trajectories that admit perfect reconstruction of bandlimited fields and are minimal in terms of the path density metric. We identify optimal sampling trajectories from a restricted class of trajectories.
Rate Distortion Behavior of Sparse Sources

The rate distortion behavior of sparse memoryless sources is studied. These serve as models of sparse signal representations and facilitate the performance analysis of “sparsifying” transforms like the wavelet transform and nonlinear approximation schemes. For strictly sparse binary sources with Hamming distortion, R(D) is shown to be almost linear. For nonstrictly sparse continuous-valued sources, termed compressible, two measures of compressibility are introduced: incomplete moments and geometric mean. The former lead to low- and high-rate upper bounds on mean squared error D(R), while the latter yields lower and upper bounds on source entropy, thereby characterizing asymptotic R(D) behavior. Thus, the notion of compressibility is quantitatively connected with actual lossy compression. These bounding techniques are applied to two source models: Gaussian mixtures and power laws matching the approximately scale-invariant decay of wavelet coefficients. The former are versatile models for sparse data, which in particular allow to bound high-rate compression performance of a scalar mixture compared to a corresponding unmixed transform coding system. Such a comparison is interesting for transforms with known coefficient decay, but unknown coefficient ordering, e.g., when positions of highest-variance coefficients are unknown. The use of these models and results in distributed coding and compressed sensing scenarios are also discussed.
Unlabeled sensing: Reconstruction algorithm and theoretical guarantees

It often happens that we are interested in reconstructing an unknown signal from partial measurements. Also, it is typically assumed that the location (temporal or spatial) of each sample is known and that the only distortion present in the observations is due to additive measurement noise. However, there are some applications where such location information is lost. In this paper, we consider the situation in which the order of noisy samples, taken from a linear measurement system, is missing. Previous work on this topic has only considered the noiseless case and exhaustive search combinatorial algorithms. We propose a much more efficient algorithm based on a geometrical viewpoint of the problem. We also study the uniqueness of the solution under different choices of the sampling matrix and its robustness to noise for the case of two-dimensional signals. Finally we provide simulation results to confirm the theoretical findings of the paper.
Super-resolution from highly undersampled images

Aliasing artifacts in images are visually very disturbing. Therefore, most imaging devices apply a low-pass filter before sampling. This removes all aliasing from the image, but it also creates a blurred image. Actually, all the image information above half the sampling frequency is removed. In this paper, we present a new method for the reconstruction of a high resolution image from a set of highly undersampled and thus aliased images. We use the information in the entire frequency spectrum, including the aliased part, to create a sharp, high resolution image. The unknown relative shifts between the images are computed using a subspace projection approach. We show that the projection can be decomposed into multiple projections onto smaller subspaces. This allows for a considerable reduction of the overall computational complexity of the algorithm. A high resolution image can then be reconstructed from the registered low resolution images. Simulation results show the validity of our algorithm.