nils-herrmann/clean_scirepeval huggingface dataset:
Distributed compression in camera sensor networks

We address the problem of distributed compression in camera sensor networks. Our approach uses some geometrical information in order to estimate the correlation in the visual data. This correlation, which is related to the structure of the plenoptic function, can then be used to reduce the overall transmission rate from the sensors to a common central receiver. Our approach allows for a flexible allocation of the bit-rates amongst the encoders and can be made resilient to a fixed number of occlusions. Finally, we show that our distributed coding approach can be extended to general binary sources. The technique we propose uses linear channel codes and can achieve any point of the Slepian-Wolf achievable rate region.
Exact Local Reconstruction Algorithms for Signals with Finite Rate of Innovation

Consider the problem of sampling signals which are not bandlimited, but still have a finite number of degrees of freedom per unit of time, such as, for example, piecewise polynomial or piecewise sinusoidal signals, and call the number of degrees of freedom per unit of time the rate of innovation. Classical sampling theory does not enable a perfect reconstruction of such signals since they are not bandlimited. In this paper, we show that many signals with finite rate of innovation can be sampled and perfectly reconstructed using kernels of compact support and a local reconstruction algorithm. The class of kernels that we can use is very rich and includes functions satisfying strang-fix conditions, exponential splines and functions with rational Fourier transforms. Extension of such results to the 2-dimensional case are also discussed and an application to image super-resolution is presented.
Sampling Piecewise Sinusoidal Signals With Finite Rate of Innovation Methods

We consider the problem of sampling piecewise sinusoidal signals. Classical sampling theory does not enable perfect reconstruction of such signals since they are not band-limited. However, they can be characterized by a finite number of parameters, namely, the frequency, amplitude, and phase of the sinusoids and the location of the discontinuities. In this paper, we show that under certain hypotheses on the sampling kernel, it is possible to perfectly recover the parameters that define the piecewise sinusoidal signal from its sampled version. In particular, we show that, at least theoretically, it is possible to recover piecewise sine waves with arbitrarily high frequencies and arbitrarily close switching points. Extensions of the method are also presented such as the recovery of combinations of piecewise sine waves and polynomials. Finally, we study the effect of noise and present a robust reconstruction algorithm that is stable down to SNR levels of 7 [dB].
Solving physics-driven inverse problems via structured least squares

Numerous physical phenomena are well modeled by partial differential equations (PDEs); they describe a wide range of phenomena across many application domains, from modeling EEG signals in electroencephalography to, modeling the release and propagation of toxic substances in environmental monitoring. In these applications it is often of interest to find the sources of the resulting phenomena, given some sparse sensor measurements of it. This will be the main task of this work. Specifically, we will show that finding the sources of such PDE-driven fields can be turned into solving a class of well-known multi-dimensional structured least squares problems. This link is achieved by leveraging from recent results in modern sampling theory - in particular, the approximate Strang-Fix theory. Subsequently, numerical simulation results are provided in order to demonstrate the validity and robustness of the proposed framework.
DAGAN: Deep De-Aliasing Generative Adversarial Networks for Fast Compressed Sensing MRI Reconstruction

Compressed sensing magnetic resonance imaging (CS-MRI) enables fast acquisition, which is highly desirable for numerous clinical applications. This can not only reduce the scanning cost and ease patient burden, but also potentially reduce motion artefacts and the effect of contrast washout, thus yielding better image quality. Different from parallel imaging-based fast MRI, which utilizes multiple coils to simultaneously receive MR signals, CS-MRI breaks the Nyquist–Shannon sampling barrier to reconstruct MRI images with much less required raw data. This paper provides a deep learning-based strategy for reconstruction of CS-MRI, and bridges a substantial gap between conventional non-learning methods working only on data from a single image, and prior knowledge from large training data sets. In particular, a novel conditional Generative Adversarial Networks-based model (DAGAN)-based model is proposed to reconstruct CS-MRI. In our DAGAN architecture, we have designed a refinement learning method to stabilize our U-Net based generator, which provides an end-to-end network to reduce aliasing artefacts. To better preserve texture and edges in the reconstruction, we have coupled the adversarial loss with an innovative content loss. In addition, we incorporate frequency-domain information to enforce similarity in both the image and frequency domains. We have performed comprehensive comparison studies with both conventional CS-MRI reconstruction methods and newly investigated deep learning approaches. Compared with these methods, our DAGAN method provides superior reconstruction with preserved perceptual image details. Furthermore, each image is reconstructed in about 5 ms, which is suitable for real-time processing.
Sampling Streams of Pulses With Unknown Shapes

This paper develops a theory for sampling and perfectly reconstructing streams of short pulses of unknown shapes in the continuous-time domain. The single pulse is modelled as the delayed version of a wavelet sparse signal, which is normally not band-limited. As the delay can be an arbitrary real number, it is difficult to develop an exact sampling result for this type of signals. We manage to achieve the exact reconstruction of the pulses by using only the knowledge of the Fourier transform of the signal at specific frequencies. We further introduce a multichannel acquisition system that uses a new family of compact-support sampling kernels for extracting the Fourier information from the samples. The shape of the kernel is independent of the wavelet basis in which the pulse is sparse, and hence the same acquisition system can be used with pulses that are sparse on different wavelet bases. By exploiting the fact that pulses have short duration and that the sampling kernels have compact support, we finally propose a local and sequential algorithm to reconstruct streaming pulses from the samples.
Sparse signal recovery using structured total maximum likelihood

In this paper, we consider the sparse signal recovery problem when the dictionary is a Fourier frame. Based on the annihilation relation, the sparse signal recovery from noisy observations is posed as a structured total maximum likelihood (STML) problem. The recent structured total least squares (STLS) approach for finite rate of innovation signal recovery can be viewed as a particular version of our method. We transform the STML problem which has an additional logdet term into a form similar to the STLS problem. It can be effectively tackled using an iterative quadratic maximum likelihood like algorithm. From simulation results, our proposed STML approach outperforms the STLS based algorithm and the state-of-the-art sparse recovery algorithms.
On the compression of two-dimensional piecewise smooth functions

It is well known that wavelets provide good non-linear approximation of one-dimensional (1-D) piecewise smooth functions. However, it has been shown that the use of a basis with good approximation properties does not necessarily lead to a good compression algorithm. The situation in 2-D is much more complicated since wavelets are not good for modeling piecewise smooth signals (where discontinuities are along smooth curves). The purpose of this work is to analyze the performance of compression algorithms for 2-D piecewise smooth functions directly in a rate distortion context. We consider some simple image models and compute rate distortion bounds achievable using oracle based methods. We then present a practical compression algorithm based on optimal quadtree decomposition that, in some cases, achieve the oracle performance.
Quadtree Structured Image Approximation for Denoising and Interpolation

The success of many image restoration algorithms is often due to their ability to sparsely describe the original signal. Shukla proposed a compression algorithm, based on a sparse quadtree decomposition model, which could optimally represent piecewise polynomial images. In this paper, we adapt this model to the image restoration by changing the rate-distortion penalty to a description-length penalty. In addition, one of the major drawbacks of this type of approximation is the computational complexity required to find a suitable subspace for each node of the quadtree. We address this issue by searching for a suitable subspace much more efficiently using the mathematics of updating matrix factorisations. Algorithms are developed to tackle denoising and interpolation. Simulation results indicate that we beat state of the art results when the original signal is in the model (e.g., depth images) and are competitive for natural images when the degradation is high.
Spatio-temporal sampling and reconstruction of diffusion fields induced by point sources

In this paper we consider a diffusion field induced by multiple point sources and address the problem of reconstructing the field from its spatio-temporal samples obtained using a sensor network. We begin by formulating the problem as a multi-source estimation problem - so estimating source locations, activation times and intensities given samples of the induced field. Next a two-step algorithm is proposed for the single (localized and instantaneous) source field. First, the source location and intensity are estimated by applying the “reciprocity gap” principle; we show that this step can also reveal locations of multiple non-instantaneous sources. In the second step, we use an iterative method, based on Cauchy-Schwarz inequality, to find the activation time given the estimated location and intensity. Finally we extend this algorithm to the multi-source field and present simulation results to validate our findings.
An Image Recapture Detection Algorithm Based on Learning Dictionaries of Edge Profiles

With today's digital camera technology, high-quality images can be recaptured from an liquid crystal display (LCD) monitor screen with relative ease. An attacker may choose to recapture a forged image in order to conceal imperfections and to increase its authenticity. In this paper, we address the problem of detecting images recaptured from LCD monitors. We provide a comprehensive overview of the traces found in recaptured images, and we argue that aliasing and blurriness are the least scene dependent features. We then show how aliasing can be eliminated by setting the capture parameters to predetermined values. Driven by this finding, we propose a recapture detection algorithm based on learned edge blurriness. Two sets of dictionaries are trained using the K-singular value decomposition approach from the line spread profiles of selected edges from single captured and recaptured images. An support vector machine classifier is then built using dictionary approximation errors and the mean edge spread width from the training images. The algorithm, which requires no user intervention, was tested on a database that included more than 2500 high-quality recaptured images. Our results show that our method achieves a performance rate that exceeds 99% for recaptured images and 94% for single captured images.
SRHRF+: Self-Example Enhanced Single Image Super-Resolution Using Hierarchical Random Forests

Example-based single image super-resolution (SISR) methods use external training datasets and have recently attracted a lot of interest. Self-example based SISR methods exploit redundant non-local self-similar patterns in natural images and because of that are more able to adapt to the image at hand to generate high quality super-resolved images. In this paper, we propose to combine the advantages of example-based SISR and self-example based SISR. A novel hierarchical random forests based super-resolution (SRHRF) method is proposed to learn statistical priors from external training images. Each layer of random forests reduce the estimation error due to variance by aggregating prediction models from multiple decision trees. The hierarchical structure further boosts the performance by pushing the estimation error due to bias towards zero. In order to further adaptively improve the super-resolved image, a self-example random forests (SERF) is learned from an image pyramid pair constructed from the down-sampled SRHRF generated result. Extensive numerical results show that the SRHRF method enhanced using SERF (SRHRF+) achieves the state-of-the-art performance on natural images and yields substantially superior performance for image with rich self-similar patterns.
Quadtree structured restoration algorithms for piecewise polynomial images

Iterative shrinkage of sparse and redundant representations are at the heart of many state of the art denoising and deconvolution algorithms. They assume the signal is well approximated by a few elements from an overcomplete basis of a linear space. If one instead selects the elements from a nonlinear manifold it is possible to more efficiently represent piecewise polynomial signals. This suggests that image restoration algorithms based around nonlinear transformations could provide better results for this class of signals. This paper uses iterative shrinkage ideas and a nonlinear quadtree decomposition to develop image restoration algorithms suitable for piecewise polynomial images.
Adaptive layer extraction for image based rendering

Image based rendering is a promising way to produce arbitrary views of a scene using images instead of object models. However, depth variations and occlusions cause blurring in the rendered images. The solution is to use some geometrical information in order to steer the interpolation filters according to the depth. The level of detail of this geometry is often predetermined. In this paper, we present a method for extracting depth layers in the presence of occlusions for image based rendering. Moreover, we show how the layer extraction can be made to estimate depth layers in an adaptive manner, based on the spectral analysis of the plenoptic function. The rendering system therefore automatically adapts the number of depth layers based on the scene and the spacing of the sample cameras.
An extension of the FRI framework for calcium transient detection

Two-photon calcium imaging of the brain allows the spatiotemporal activity of neuronal networks to be monitored at cellular resolution. In order to analyse this activity it must first be possible to detect, with high temporal resolution, spikes from the time series corresponding to single neurons. Previous work has shown that finite rate of innovation (FRI) theory can be used to reconstruct spike trains from noisy calcium imaging data. In this paper we extend the FRI framework for spike detection from calcium imaging data to encompass data generated by a larger class of calcium indicators, including the genetically encoded indicator GCaMP6s. Furthermore, we implement least squares model-order estimation and perform a noise reduction procedure ('pre-whitening') in order to increase the robustness of the algorithm. We demonstrate high spike detection performance on real data generated by GCaMP6s, detecting 90% of electrophysiologically-validated spikes.
Exact sampling results for signals with finite rate of innovation using Strang-Fix conditions and local kernels

Recently, it was shown that it is possible to sample classes of signals with finite rate of innovation. These sampling schemes, however, use kernels with infinite support and this leads to complex and unstable reconstruction algorithms. In this paper, we show that many signals with finite rate of innovation can be sampled and perfectly reconstructed using kernels of compact support and a local reconstruction algorithm. The class of kernels that we can use is very rich and includes any function satisfying Strang-Fix conditions, exponential splines and functions with rational Fourier transforms.
Finite rate of innovation with non-uniform samples

In this paper, we investigate the problem of retrieving the innovation parameters (time and amplitude) of a stream of Diracs from non-uniform samples taken with a novel kernel (a hyperbolic secant). We devise a non-iterative, exact algorithm that allows perfect reconstruction of 2K innovations from as few as 2K non-uniform samples. We also investigate noise issues and compute the Cramér-Rao lower bounds for this problem. A simple total least-squares extension of the algorithm proves to be efficient in reconstructing the location of a single Dirac from noisy measurements.
On the Spectrum of the Plenoptic Function

The plenoptic function is a powerful tool to analyze the properties of multi-view image data sets. In particular, the understanding of the spectral properties of the plenoptic function is essential in many computer vision applications, including image-based rendering. In this paper, we derive for the first time an exact closed-form expression of the plenoptic spectrum of a slanted plane with finite width and use this expression as the elementary building block to derive the plenoptic spectrum of more sophisticated scenes. This is achieved by approximating the geometry of the scene with a set of slanted planes and evaluating the closed-form expression for each plane in the set. We then use this closed-form expression to revisit uniform plenoptic sampling. In this context, we derive a new Nyquist rate for the plenoptic sampling of a slanted plane and a new reconstruction filter. Through numerical simulations, on both real and synthetic scenes, we show that the new filter outperforms alternative existing filters.
Footprints and edgeprints for image denoising and compression

Wavelets have been quite successful in compression or denoising applications. To further improve the performance of wavelet based algorithms, we have recently introduced the notion of footprint, which is a data structure which contains all the wavelet coefficients generated by a discontinuity. The combined use of wavelets and footprints leads to very efficient algorithms for compression and denoising of 1D piecewise smooth signals. We extend some of the previous results by presenting a new denoising algorithm, where footprints are chosen adaptively according to the singularity locations. This new algorithm outperforms previously proposed ones. Then, we introduce the notion of edgeprints, which represents a natural extension of footprints to the two dimensional case. First experimental results on the compression of 2D piecewise smooth signals using edgeprints are promising.
Distributed sampling and compression of scenes with finite rate of innovation in camera sensor networks

We study the problem of distributed sampling and compression in sensor networks when the sensors are digital cameras that acquire a 3-D visual scene of interest from different viewing positions. We assume that sensors cannot communicate among themselves, but can process their acquired data and transmit it to a common central receiver. The main task of the receiver is then to reconstruct the best possible estimation of the original scene and the natural issue, in this context, is to understand the interplay in the reconstruction between sampling and distributed compression. In this paper, we show that if the observed scene belongs to the class of signals that can be represented with a finite number of parameters, we can determine the minimum number of sensors that allows perfect reconstruction of the scene. Then, we present a practical distributed coding approach that leads to a rate-distortion behaviour at the decoder that is independent of the number of sensors, when this number increases beyond the critical sampling. In other words, we show that the distortion at the decoder does not depend on the number of sensors used, but only on the total number of bits that can be transmitted from the sensors to the receiver
Sampling Moments and Reconstructing Signals of Finite Rate of Innovation: Shannon Meets Strang–Fix

Consider the problem of sampling signals which are not bandlimited, but still have a finite number of degrees of freedom per unit of time, such as, for example, nonuniform splines or piecewise polynomials, and call the number of degrees of freedom per unit of time the rate of innovation. Classical sampling theory does not enable a perfect reconstruction of such signals since they are not bandlimited. Recently, it was shown that, by using an adequate sampling kernel and a sampling rate greater or equal to the rate of innovation, it is possible to reconstruct such signals uniquely . These sampling schemes, however, use kernels with infinite support, and this leads to complex and potentially unstable reconstruction algorithms. In this paper, we show that many signals with a finite rate of innovation can be sampled and perfectly reconstructed using physically realizable kernels of compact support and a local reconstruction algorithm. The class of kernels that we can use is very rich and includes functions satisfying Strang-Fix conditions, exponential splines and functions with rational Fourier transform. This last class of kernels is quite general and includes, for instance, any linear electric circuit. We, thus, show with an example how to estimate a signal of finite rate of innovation at the output of an RC circuit. The case of noisy measurements is also analyzed, and we present a novel algorithm that reduces the effect of noise by oversampling
Dfferent - distributed and fully flexible image encoders for camera sensor networks

In this paper, we propose a practical coding approach for the problem of distributed compression of multi-view images. Our coding technique is based on a tree structured compression algorithm that guarantees an optimal rate-distortion behaviour for piecewise polynomial signals. We model the different views using a piecewise polynomial function whose singularity positions are shifted from one view to the others according to the constraints imposed by the structure of the plenoptic function. We show that, starting from the optimal tree decompositions of the different views, only partial information from each tree is necessary at the decoder in order to reconstruct all the different approximations. We first present our approach in the more intuitive 1D case and show that it can be used with arbitrary bit-rate allocation. Then, we propose a construction for the case of TV different views that satisfies a certain bit conservation principle. Finally, we show how our approach can be extended to the 2D case.
Single and multichannel sampling of bilevel polygons using exponential splines

In this paper we present a novel approach for sampling and reconstructing any K-sided convex and bilevel polygon with the use of exponential splines . It will be shown that with K+1 projections we are able to perfectly reconstruct a K-sided bilevel polygon from its samples. We will also investigate the multichannel sampling scenario, consisting of a bank of E-spline filters, each with a different delay parameter compared to the reference signal. We show how by retrieving the delay parameters, we can symmetrically sample and reconstruct a given bilevel polygon using exponential splines.