nils-herrmann/clean_scirepeval huggingface dataset:
Robust image recapture detection using a K-SVD learning approach to train dictionaries of edge profiles

A professionally recaptured image from an LCD monitor can be, visually, very difficult to distinguish from its original counterpart. In this paper we show that it is possible to detect a recaptured image from the unique nature of the edge profiles present in the image. We leverage the fact that the edge profiles of single and recaptured images are markedly different and we train two alternative dictionaries using the K-SVD approach. One dictionary is trained to provide a sparse representation of single captured edges and a second for recaptured edges. Using these two learned dictionaries, we can determine whether a query image has been recaptured. We achieve this by observing the type of dictionary that gives the smallest error in a sparse representation of the edges of the query image. Experiments conducted show that the proposed algorithm is capable of detecting recaptured images with a high level of accuracy and copes well with a wide range of natural images.
On compression using the distributed Karhunen-Loeve transform

In this paper, we discuss a framework for the distributed compression of vector sources, based on our previous work on distributed transform coding (2002, 2003). In particular, our goal is to develop a strategy of first applying a suitable distributed Karhunen-Loeve transform, whereafter each component can be handled by standard distributed compression techniques. In the present paper, we first study the scenario where all but one terminal furnish a noisy approximation of their observation. For the case where the underlying vector is Gaussian, and the added noise is also Gaussian, we establish that indeed it is optimal for the last terminal to apply a (local) transform to its observations, and to separately compress each component in the transform domain. Then, we outline how this leads to a general simple distributed compression strategy for Gaussian vector sources: each terminal applies a suitable local transform to its observations, and encodes the resulting components separately in a Wyner-Ziv fashion, i.e., treating the compressed descriptions of all other terminals as side information available to the decoder. This achieves the best known performance. The optimum performance in unknown to date.
Sampling schemes for 2-D signals with finite rate of innovation using kernels that reproduce polynomials

In this paper, we propose new sampling schemes for classes of 2-D signals with finite rate of innovation (FRI). In particular, we consider sets of 2-D Diracs and bilevel polygons. As opposed to using only sine or Gaussian kernels [I. Maravic et al, 2004], we allow the sampling kernel to be any function that reproduces polynomials. In the proposed sampling schemes, we exploit the polynomial approximation properties of the sampling kernels in association with other relevant techniques such as complex-moments [P. Milanfar et al, 1995], annihilating filter method [M. Vetterli et al, 2002], and directional derivatives. Specifically, for the bilevel polygons, we propose two different methods: the first uses a global reconstruction algorithm and complex moments, while the second is based on directional derivatives and local reconstruction algorithms. The trade-off between these two reconstruction modalities is also briefly discussed.
Tomographic Approach for Sampling Multidimensional Signals with Finite Rate of Innovation

Recently, it was shown that it is possible to sample classes of 1-D and 2-D signal with finite rate of innovation (FRI). In particular, in P. Shukla et al., (2005), we presented local and global schemes for sampling sets of Diracs and bilevel polygons using compactly supported kernels that reproduce polynomials. In sequel to P. Shukla et al., (2005), in this paper, we present a Radon transform based hybrid scheme for sampling more general 2-D FRI signals such as piecewise polynomials with polygonal boundaries, and higher dimensional Diracs and bilevel polytopes. The key feature of the proposed scheme is an annihilating-filter-based-back-projection (AFBP) algorithm.
Directionlets: anisotropic multidirectional representation with separable filtering

In spite of the success of the standard wavelet transform (WT) in image processing in recent years, the efficiency of its representation is limited by the spatial isotropy of its basis functions built in the horizontal and vertical directions. One-dimensional (1-D) discontinuities in images (edges and contours) that are very important elements in visual perception, intersect too many wavelet basis functions and lead to a nonsparse representation. To efficiently capture these anisotropic geometrical structures characterized by many more than the horizontal and vertical directions, a more complex multidirectional (M-DIR) and anisotropic transform is required. We present a new lattice-based perfect reconstruction and critically sampled anisotropic M-DIR WT. The transform retains the separable filtering and subsampling and the simplicity of computations and filter design from the standard two-dimensional WT, unlike in the case of some other directional transform constructions (e.g., curvelets, contourlets, or edgelets). The corresponding anisotropic basis functions (directionlets) have directional vanishing moments along any two directions with rational slopes. Furthermore, we show that this novel transform provides an efficient tool for nonlinear approximation of images, achieving the approximation power O(N/sup -1.55/), which, while slower than the optimal rate O(N/sup -2/), is much better than O(N/sup -1/) achieved with wavelets, but at similar complexity.
Transform coder identification based on quantization footprints and lattice theory

Transform coding is routinely used for lossy compression of discrete sources with memory. The input signal is divided into N -dimensional vectors, which are transformed by means of a linear mapping. Then, transform coefficients are quantized and entropy coded. In this paper we consider the problem of identifying the transform matrix as well as the quantization step sizes. We study the challenging case in which the only available information is a set of P transform decoded vectors. We formulate the problem in terms of finding the lattice with the largest determinant that contains all observed vectors. We propose an algorithm that is able to find the optimal solution and we formally study its convergence properties. Our analysis shows that it is possible to identify successfully both the transform and the quantization step sizes when P ≥ N + δ where δ is a small integer, and the probability of failure decreases exponentially to zero as P −N increases.
Approximation power of directionlets

In spite of the success of the standard wavelet transform (WT) in image processing, the efficiency of its representation is limited by the spatial isotropy of its basis functions built in only horizontal and vertical directions. One-dimensional (1-D) discontinuities in images (edges and contours), which are very important elements in visual perception, intersect too many wavelet basis functions and reduce the sparsity of the representation. To capture efficiently these anisotropic geometrical structures, a more complex multi-directional (M-DIR) and anisotropic transform is required. We present a new lattice-based perfect reconstruction and critically sampled anisotropic M-DIR WT (with the corresponding basis functions called directionlets) that retains the separable filtering and simple filter design from the standard two-dimensional (2-D) WT and imposes directional vanishing moments (DVM). Further-more, we show that this novel transform has non-linear approximation efficiency competitive to the other previously proposed over-sampled transform constructions.
Directional wavelet transforms and frames

The application of the wavelet transform in image processing is most frequently based on a separable transform. Lines and columns in an image are treated independently and the basis functions are simply products of corresponding one-dimensional functions. Such a method keeps simplicity in design and computation. A new two-dimensional approach is proposed, which retains the simplicity of separable processing, but allows more directionalities. The method can be applied in many areas like denoising, nonlinear approximation and compression. The results on nonlinear approximation and denoising show interesting gains compared to the standard two-dimensional analysis.
Sampling Curves With Finite Rate of Innovation

In this paper, we extend the theory of sampling signals with finite rate of innovation (FRI) to a specific class of two-dimensional curves, which are defined implicitly as the zeros of a mask function. Here the mask function has a parametric representation as a weighted summation of a finite number of complex exponentials, and therefore, has finite rate of innovation . An associated edge image, which is discontinuous on the predefined parametric curve, is proved to satisfy a set of linear annihilation equations. We show that it is possible to reconstruct the parameters of the curve (i.e., to detect the exact edge positions in the continuous domain) based on the annihilation equations. Robust reconstruction algorithms are also developed to cope with scenarios with model mismatch. Moreover, the annihilation equations that characterize the curve are linear constraints that can be easily exploited in optimization problems for further image processing (e.g., image up-sampling). We demonstrate one potential application of the annihilation algorithm with examples in edge-preserving interpolation. Experimental results with both synthetic curves as well as edges of natural images clearly show the effectiveness of the annihilation constraint in preserving sharp edges, and improving SNRs.
Sparse Representation in Fourier and Local Bases Using ProSparse: A Probabilistic Analysis

Finding the sparse representation of a signal in an overcomplete dictionary has attracted a lot of attention over the past years.  Unlike our previous work, which establishes deterministic (worst-case) sparsity bounds for ProSparse to succeed, this paper presents a probabilistic average-case analysis of the algorithm. Based on a generating-function approach, closed-form expressions for the exact success probabilities of ProSparse are given. The success probabilities are also analyzed in the high-dimensional regime. This asymptotic analysis characterizes a sharp phase transition phenomenon regarding the performance of the algorithm.
Wavelet transform footprints: catching singularities for compression and denoising

Wavelets have been widely used for signal compression, image compression being a prime example, and for signal denoising. What makes wavelets such an attractive tool is their capability of representing both transient and stationary behaviors of a signal with few coefficients. We consider the problem of compressing and denoising a particular class of functions: piecewise polynomial signals. We show the limit of usual wavelet coders and present an alternative compression algorithm. The main innovation of the algorithm is that it tries to efficiently compress the significant coefficients of the wavelet decomposition rather then the zero coefficients as in usual coders. The proposed algorithm can potentially be extended to more general signals and represents an effective solution to problems like signal denoising and image compression.
Sparse graph signal reconstruction and image processing on circulant graphs

In this work, we present extensions of the framework of sampling and reconstructing signals with a finite rate of innovation (FRI) to the graph domain, by tackling the problem of _R"-sparse graph signal reconstruction on perturbed circulant graphs, simulating network clusters within a large network. Given a dimensionality-reduced approximation of the GFT of the original graph signal, we develop a reconstruction approach, whereby, we operate on each subgraph individually using a set of approximation and denoising schemes. In particular, we employ a variation of Prony's method with Cadzow's algorithm, and further iterative denoising, which can lead to perfect reconstruction. In addition, we extend the application of recently developed circulant graph-wavelet fllterbanks to images featuring patterns, in a novel model inspired by image segmentation, which involves a localized operation of the graph wavelet transform on individual segments of homogeneous intensity content, employing the nearest circulant matrix approximation scheme. The proposed method outperforms traditional methods in the classical domain in nonlinear approximation performance. We give preliminary results and discuss generalizations to arbitrary graphs.
Reconstructing non-point sources of diffusion fields using sensor measurements

We present a framework for estimating non-localized sources of diffusion fields using spatiotemporal measurements of the field. Specifically in this contribution, we consider two non-localized source types: straight line and polygonal sources and assume that the induced field is monitored using a sensor network. Given the sensor measurements, we demonstrate, for each non-point source parameterization, how to reduce the source estimation problem to a system governed by a power series expansion that can then be efficiently solved using Prony's method, in order to reconstruct the source. We then evaluate the proposed algorithms by performing some numerical simulations using both noiseless and noisy spatiotemporal sensor measurements of the field.
Object recognition using multi-view imaging

Difficult situations such as high noise or low resolution can seriously degrade the performance of object recognition algorithms that operate on isolated images. We show that recognition performance may be improved substantially in such cases by fusing the information available from a sequence of multi-view images. In this paper we present two algorithms for object recognition based on SIFT feature points. The first operates on single images and uses chirality constraints to reduce the recognition errors that arise when only a small number of feature points are matched. The procedure is extended in the second algorithm which operates on a multi-view image sequence and, by tracking feature points in the plenoptic domain, is able to fuse feature point matches from all the available images resulting in more robust recognition.
Plenoptic Layer-Based Modeling for Image Based Rendering

Image based rendering is an attractive alternative to model based rendering for generating novel views because of its lower complexity and potential for photo-realistic results. To reduce the number of images necessary for alias-free rendering, some geometric information for the 3D scene is normally necessary. In this paper, we present a fast automatic layer-based method for synthesizing an arbitrary new view of a scene from a set of existing views. Our algorithm takes advantage of the knowledge of the typical structure of multiview data to perform occlusion-aware layer extraction. In addition, the number of depth layers used to approximate the geometry of the scene is chosen based on plenoptic sampling theory with the layers placed non-uniformly to account for the scene distribution. The rendering is achieved using a probabilistic interpolation approach and by extracting the depth layer information on a small number of key images. Numerical results demonstrate that the algorithm is fast and yet is only 0.25 dB away from the ideal performance achieved with the ground-truth knowledge of the 3D geometry of the scene of interest. This indicates that there are measurable benefits from following the predictions of plenoptic theory and that they remain true when translated into a practical system for real world data.
Sequential local FRI sampling of infinite streams of Diracs

The theory of sampling signals with finite rate of innovation (FRI) has shown that it is possible to perfectly recover classes of non-bandlimited signals such as streams of Diracs from uniform samples. Most of previous papers, however, have to some extent only focused on the sampling of periodic or finite duration signals. In this paper we propose a novel method that is able to reconstruct infinite streams of Diracs, even in high noise scenarios. We sequentially process the discrete samples and output locations and amplitudes of the Diracs in real-time. We first establish conditions for perfect reconstruction in the noiseless case and then present the sequential algorithm for the noisy scenario. We also show that we can achieve a high reconstruction accuracy of 1000 Diracs for SNRs as low as 5dB.
Multiview Image Coding Using Depth Layers and an Optimized Bit Allocation

In this paper, we present a novel wavelet-based compression algorithm for multiview images. This method uses a layer-based representation, where the 3-D scene is approximated by a set of depth planes with their associated constant disparities. The layers are extracted from a collection of images captured at multiple viewpoints and transformed using the 3-D discrete wavelet transform (DWT). The DWT consists of the 1-D disparity compensated DWT across the viewpoints and the 2-D shape-adaptive DWT across the spatial dimensions. Finally, the wavelet coefficients are quantized and entropy coded along with the layer contours. To improve the rate-distortion performance of the entire coding method, we develop a bit allocation strategy for the distribution of the available bit budget between encoding the layer contours and the wavelet coefficients. The achieved performance of our proposed scheme outperforms the state-of-the-art codecs for several data sets of varying complexity.
Tilted layer-based modeling for enhanced light-field processing and image based rendering

Image based rendering is an attractive approach for novel view synthesis due to its low complexity requirements and potential for photorealistic results. However for successful rendering, geometric priors about the structure of the scene are necessary. In this paper we present a tilted layer model approximation of the plenoptic function which gives improved modeling of scenes where the objects are not fronto-parallel to the camera views while preserving occlusion ordering. The framework is extended to the case where camera positions are not constrained to a single plane but can lie on multiple planes. Results on the Middlebury dataset and simulated scenes show that better rendering results can be obtained compared with the state-of-the-art using a fronto-parallel layer model, or alternatively similar results can be obtained with a more compact layer representation of the scene.
Exact Feature Extraction Using Finite Rate of Innovation Principles With an Application to Image Super-Resolution

The accurate registration of multiview images is of central importance in many advanced image processing applications. Image super-resolution, for example, is a typical application where the quality of the super-resolved image is degrading as registration errors increase. Popular registration methods are often based on features extracted from the acquired images. The accuracy of the registration is in this case directly related to the number of extracted features and to the precision at which the features are located: images are best registered when many features are found with a good precision. However, in low-resolution images, only a few features can be extracted and often with a poor precision. By taking a sampling perspective, we propose in this paper new methods for extracting features in low-resolution images in order to develop efficient registration techniques. We consider, in particular, the sampling theory of signals with finite rate of innovation and show that some features of interest for registration can be retrieved perfectly in this framework, thus allowing an exact registration. We also demonstrate through simulations that the sampling model which enables the use of finite rate of innovation principles is well suited for modeling the acquisition of images by a camera. Simulations of image registration and image super-resolution of artificially sampled images are first presented, analyzed and compared to traditional techniques. We finally present favorable experimental results of super-resolution of real images acquired by a digital camera available on the market.
Analysis of optimal filter banks for multiple description coding

We study the problem of multiple description (MD) coding of stationary Gaussian sources with memory. First, we compute an approximate rate distortion region for these sources, which we prove to be asymptotically tight at high rates: this region generalizes the standard MD rate distortion region for memoryless sources. Then we develop an algorithm for the design of optimal biorthogonal filter banks for MD coding. Finally. We present some experimental results, where we measure the deviation from optimality of our proposed system. For almost uncorrelated sources the gap between the performance of our proposed system and the ideal bounds is quite high, on the other hand for highly correlated sources this gap is reduced, due to the ability of our system to take advantage of the memory in the source. In this case, in realistic scenarios where finite complexity/delay is an issue, the subband coding approach is competitive with other approaches such as the decorrelating transform followed by MD scalar quantizers.
Distributed Acquisition and Image Super-Resolution Based on Continuous Moments from Samples

Recently, new sampling schemes were presented for signals with finite rate of innovation (FRI) using sampling kernels reproducing polynomials or exponentials. In this paper, we extend those sampling schemes to a distributed acquisition architecture in which numerous and randomly located sensors are pointing to the same area of interest. We emphasize the importance played by moments and show how to acquire efficiently FRI signals with a set of sensors. More importantly, we also show that those sampling schemes can be used for accurate registration of affine transformed and low-resolution images. Based on this, a new super-resolution algorithm was developed and showed good preliminary results.
Filter bank frame expansions with erasures

We study frames for robust transmission over the Internet. In our previous work, we used quantized finite-dimensional frames to achieve resilience to packet losses; here, we allow the input to be a sequence in l/sub 2/(Z) and focus on a filter-bank implementation of the system. We present results in parallel, R/sup N/ or C/sup N/ versus l/sub 2/(Z), and show that uniform tight frames, as well as newly introduced strongly uniform tight frames, provide the best performance.
Interactive multiview image coding

We propose a novel multiview compression method for multiview images. The algorithm supports random access for interactive applications and has low storage requirements. The fundamental component of the method is the layer-based representation, which partitions the dataset into redundant layers characterized by a constant depth value. We exploit the redundant property of each layer and remove the side information uncertainty using Distributed Source Coding (DSC) principles. In comparison to independent coding, our method achieves a PSNR improvement of 3dB. Furthermore, we present a rate-distortion (RD) analysis which demonstrates that the proposed algorithm can achieve a better performance in comparison to independent coding.