nils-herrmann/clean_scirepeval huggingface dataset:
A New Estimator for Image Denoising Using a 2D Dual-Tree M-Band Wavelet Decomposition

We propose a new estimator for image denoising using a 2D dual-tree M-band wavelet transform. Our work extends existing block-based wavelet thresholding methods by exploiting simultaneously coefficients in the two M-band wavelet trees. The contributions of this paper are two-fold. Firstly, we perform a statistical analysis of the noise in the considered redundant decomposition. Secondly, we propose an efficient method to remove the noise. Our approach relies on an extension of Stein's formula which allows us to take into account the specific correlations of the noise components. Simulation results are then presented to validate the proposed method
HOGMep: Variational Bayes and higher-order graphical models applied to joint image recovery and segmentation

Variational Bayesian approaches have been successfully applied to image segmentation. They usually rely on a Potts model for the hidden label variables and a Gaussian assumption on pixel intensities within a given class. Such models may however be limited, especially in the case of multicomponent images. We overcome this limitation with HOGMep, a Bayesian formulation based on a higher-order graphical model (HOGM) on labels and a Multivariate Exponential Power (MEP) prior for intensities in a class. Then, we develop an efficient statistical estimation method to solve the associated problem. Its flexibility accommodates to a broad range of applications, demonstrated on multicomponent image segmentation and restoration.
BRANE Clust: Cluster-Assisted Gene Regulatory Network Inference Refinement

Discovering meaningful gene interactions is crucial for the identification of novel regulatory processes in cells. Building accurately the related graphs remains challenging due to the large number of possible solutions from available data. Nonetheless, enforcing a priori on the graph structure, such as modularity, may reduce network indeterminacy issues. BRANE Clust (Biologically-Related A priori Network Enhancement with Clustering) refines gene regulatory network (GRN) inference thanks to cluster information. It works as a post-processing tool for inference methods (i.e., CLR, GENIE3). In BRANE Clust, the clustering is based on the inversion of a system of linear equations involving a graph-Laplacian matrix promoting a modular structure. Our approach is validated on DREAM4 and DREAM5 datasets with objective measures, showing significant comparative improvements. We provide additional insights on the discovery of novel regulatory or co-expressed links in the inferred Escherichia coli network evaluated using the STRING database. The comparative pertinence of clustering is discussed computationally (SIMoNe, WGCNA, X-means) and biologically (RegulonDB). BRANE Clust software is available at:  http://www-syscom.univ-mlv.fr/~pirayre/Codes-GRN-BRANE-clust.html.
Seismic multiple removal with a primal-dual proximal algorithm

Both random and structured perturbations affect seismic data. Their removal, to unveil meaningful geophysical information, requires additional priors. Seismic multiples are one form of structured perturbations related to wave-field bouncing. In this paper, we model these undesired signals through a time-varying filtering process accounting for inaccuracies in amplitude, time-shift and average frequency of available templates. We recast the problem of jointly estimating the filters and the signal of interest (primary) in a new convex variational formulation, allowing the incorporation of knowledge about the noise statistics. By making some physically plausible assumptions about the slow time variations of the filters, and by adopting a potential promoting the sparsity of the primary in a wavelet frame, we design a primal-dual algorithm which yields good performance in the provided simulation examples.
2D dual-tree M-band wavelet decomposition

We propose a 2D generalization to the M-band case of the dual-tree structure (initially proposed by N.G. Kingsbury and further investigated by I.W. Selesnick) based on a Hilbert pair of wavelets. We particularly address the construction of the dual basis and the resulting directional analysis. We revisit the necessary preprocessing stage in the M-band case. While several reconstructions are possible because of the redundancy of the representation, we propose a new optimal signal reconstruction technique, which minimizes potential estimation errors. The effectiveness of the proposed M-band decomposition is demonstrated via image denoising comparisons.
Optimization of Synthesis Oversampled Complex Filter Banks

An important issue with oversampled FIR analysis filter banks (FBs) is to determine inverse synthesis FBs, when they exist. Given any complex oversampled FIR analysis FB, we first provide an algorithm to determine whether there exists an inverse FIR synthesis system. We also provide a method to ensure the Hermitian symmetry property on the synthesis side, which is serviceable to processing real-valued signals. As an invertible analysis scheme corresponds to a redundant decomposition, there is no unique inverse FB. Given a particular solution, we parameterize the whole family of inverses through a null space projection. The resulting reduced parameter set simplifies design procedures, since the perfect reconstruction constrained optimization problem is recast as an unconstrained optimization problem. The design of optimized synthesis FBs based on time or frequency localization criteria is then investigated, using a simple yet efficient gradient algorithm.
Noise reduction of images with multiple subband transforms

It is reported that the use of multiple subband transforms for thresholding based denoising gains performance in the mean square error sense. In traditional thresholding-based methods, a noisy image is decomposed by linear transformations such as wavelets, FFT and so on and the transformed coefficients are hard/soft-thresholded. In particular, it is well-known that wavelets work well for denoising. From the viewpoint that wavelets are in a class of subband transforms, we propose a strategy in which multiple subband transforms are switched region by region, i.e. block by block. For reconstruction, the projection-based iterative method is used. Experimental results are pretty good and promising.
Seismic data compression using GULLOTS

Previous work has shown that GenLOT coding is a very effective technique for compressing seismic data. The role of a transform in a coder is to concentrate information and reduce statistical redundancy. When used with embedded zerotree coding, GenLOTs often provide superior performance to traditional block oriented algorithms or to wavelets. In this work we investigate the use of generalized unequal length lapped orthogonal transforms (GULLOT). Their shorter bases for high-frequency components are suitable for reducing ringing artifacts in images. While GULLOTs yield comparable performance to GenLOTs on smooth seismic signals like stacked sections, they achieve improved performance on less smooth signals such as shot gathers.
Euclid in a Taxicab: Sparse Blind Deconvolution with Smoothed ${\ell _1}/{\ell _2}$ Regularization

The ℓ<sub>1</sub>/ℓ<sub>2</sub> ratio regularization function has shown good performance for retrieving sparse signals in a number of recent works, in the context of blind deconvolution. Indeed, it benefits from a scale invariance property much desirable in the blind context. However, the ℓ<sub>1</sub>/ℓ<sub>2</sub> function raises some difficulties when solving the nonconvex and nonsmooth minimization problems resulting from the use of such a penalty term in current restoration methods. In this paper, we propose a new penalty based on a smooth approximation to the ℓ<sub>1</sub>/ℓ<sub>2</sub> function. In addition, we develop a proximal-based algorithm to solve variational problems involving this function and we derive theoretical convergence results. We demonstrate the effectiveness of our method through a comparison with a recent alternating optimization strategy dealing with the exact ℓ<sub>1</sub>/ℓ<sub>2</sub> term, on an application to seismic data blind deconvolution.
2D Dual-Tree Complex Biorthogonal M-Band Wavelet Transform

Dual-tree wavelet transforms have recently gained popularity since they provide low-redundancy directional analyses of images. In our recent work, dyadic real dual-tree decompositions have been extended to the M-band case, so adding much flexibility to this analysis tool. In this work, we propose to further extend this framework on two fronts by considering (i) biorthogonal and (ii) complex M-band dual-tree decompositions. Denoising results are finally provided to demonstrate the validity of the proposed design rules.
Denoising of seismic signals with oversampled filter banks

In several applications such as denoising, when signal expansion is not crucial, oversampled filter banks may outperform critically decimated filter banks. We study the performance of the recently proposed GLPBT (Generalized Lapped Pseudo-Biorthogonal Transform), a class of oversampled filter banks, on noise removal in seismic data, using controlled redundancy. We also investigate heuristics for the choice of an optimal threshold, which appears to depend non-trivially on the noise variance. Tests indicate that carefully designed oversampled filter banks are able to outperform critically sampled ones.
Algorithm Comparison for Real Time Knock Detection

This paper addresses the implementation and comparison of algorithms for real-time knock detection. Knock is an unwanted abnormal combustion process that may damage engines and limit their efficiency. For series vehicles, knock detection is generally obtained from knock sensors that capture other noise sources, thus requiring robust algorithms. In order to estimate the performance of time-frequency and Kalman filter based algorithms, a knock signal model is proposed and the algorithms are tested under various noise conditions. Experiments on modelled and real signals show the superiority of the recently developed S-method with respect to the extended Kalman filtering.
Noise Covariance Properties in Dual-Tree Wavelet Decompositions

Dual-tree wavelet decompositions have recently gained much popularity, mainly due to their ability to provide an accurate directional analysis of images combined with a reduced redundancy. When the decomposition of a random process is performed-which occurs in particular when an additive noise is corrupting the signal to be analyzed-it is useful to characterize the statistical properties of the dual-tree wavelet coefficients of this process. As dual-tree decompositions constitute over-complete frame expansions, correlation structures are introduced among the coefficients, even when a white noise is analyzed. In this paper, we show that it is possible to provide an accurate description of the covariance properties of the dual-tree coefficients of a wide-sense-stationary process. The expressions of the (cross-) covariance sequences of the coefficients are derived in the one- and two-dimensional cases. Asymptotic results are also provided, allowing to predict the behavior of the second-order moments for large lag values or at coarse resolution. In addition, the cross-correlations between the primal and dual wavelets, which play a primary role in our theoretical analysis, are calculated for a number of classical wavelet families. Simulation results are finally provided to validate these results.
A Visual Analytics Framework to Study Honey Bee Behaviour

We present an end-to-end visual analytics framework that aims to facilitate prediction and decision making about honey bee health based on micro sensing data. The framework is particularly tailored to cope with heterogeneous data from micro sensors and environmental sensors that are deployed to collect information about bees and their environment. The framework design allows for a wide range of end users, including scientists, bee keepers, and decision makers, to effectively explore the bee data through interactive visual interfaces. User centred design is deployed throughout the development to meet the various requirements of the users. A large scale study is being planned to evaluate and further refine the framework based on user experiences.
On confidence and response times of human observers in subjective image quality assessment

Mean opinion scores obtained in subjective image quality experiments are widely accepted as measures of perceived visual quality. They have, however, a strong limitation regarding the reliability of the rated quality, since there is no explicit information as to whether the human observer experienced difficulties when judging image quality. We thus suggest that additional information about the observers confidence should be provided along with the actual quality measure. In this paper, we analyse two ways of obtaining this confidence measure; firstly as a confidence score given by the human observer and secondly as an indirect measure of the observers response time to provide the quality score. We reveal strong relationships of confidence scores and response times to the quality scores. We further propose a model to predict observer confidence based on the quality scores and response times.
Perceptual evaluation of motion JPEG2000 quality over wireless channels

In this paper, we investigate the applicability of perceptual image quality metrics for quality assessment of motion JPEG2000 (MJ2) video streams over wireless channels. A performance evaluation of MJ2 quality for different levels of source compression and channel coding settings is provided. This reveals insights into the characteristics and suitability of the considered perceptual quality metrics for wireless video quality assessment. In particular, a reduced-reference hybrid image quality metric (HIQM) is identified as the most favorable metric with respect to supporting real-time applications. The findings obtained from the considered scenarios may also guide the design of efficient physical layer functions for wireless video systems
Design of Environmental Sensor Networks Using Evolutionary Algorithms

An evolutionary algorithm (EA)-assisted spatial sampling methodology is proposed to assist decision makers in sensor network (SN) deployments. We incorporated an interpolation technique with leave-one-out cross-validation (LOOCV) to assess the representativeness of a particular SN design. For the validation of our method, we utilized Tasmania's South Esk Hydrological Model developed by the Commonwealth Scientific and Industrial Research Organisation, which includes a range of environmental variables describing the landscape. We demonstrated that our proposed methodology is capable of assisting in the initial design of SN deployment.  Kriging is shown to be the best suited spatial interpolation algorithm for the EA's LOOCV under the current empirical study.
Deblurring Filter Design Based on Fuzzy Regression Modeling and Perceptual Image Quality Assessment

Images captured by digital cameras are generally not perfect as image blurring is usually generated by camera motion through long hand-held exposure. Deblurring filters can be used to improve image quality by removing image blur. Prior to develop a deblurring filter, a simulator for image quality assessment is essential to optimize filter parameters. Although subjective image quality assessment (subjective IQA) is commonly used for evaluating the visual effect of digital images for a wide range of image processing applications, it is inconvenient to be implemented in real-time. Generally, statistical regression is used to generate a functional map to correlate the subjective IQA and the objective image quality metrics. However, it cannot address the uncertainty caused by human judgment during the subjective IQA. This paper first proposes a fuzzy regression method to develop the functional map that overcomes the limitation of statistical regression that cannot account for uncertainty introduced through human judgment. Based on the fuzzy regression models, the deblurring filter parameters can be optimized. Experimental results show that the satisfactory deblurring can be achieved on blurred images captured by a smartphone camera.
How Does Image Content Affect the Added Value of Visual Attention in Objective Image Quality Assessment?

Our previous research has demonstrated that adding natural scene saliency (NSS) obtained from eye-tracking data may improve an objective metric's performance in predicting perceived image quality. In this letter, we further investigate the image content dependency of this improvement. Results show that the variation in saliency between observers highly depends on image content, and that this variation predicts the extent to which a certain image may profit from adding saliency in the objective image quality assessment.
Visual fixation patterns in subjective quality assessment: The relative impact of image content and structural distortions

The viewing behaviour of human observers during image quality assessment is analysed. In this respect, the relative impact of image content and structural distortions is of particular interest. Two subjective experiments were conducted as a basis for analysis; a region-of-interest (ROI) experiment and an eye tracking experiment under quality assessment task. A correlation analysis and a receiver operating characteristics analysis reveal, that quality assessment takes place mainly within the ROI, indicating a higher impact of the content as compared to the distortions. This seems to be only contradicted by very strong and localised distortions outside the ROI.
Applicability of UDP-lite for voice over IP in UMTS networks

This paper examines the application of UDP-lite for unequal error detection in packet-switched speech transmission via Internet protocols (voice-over-IP) over UMTS radio channels. Traditionally, UDP is used as transport layer protocol, which contains a checksum that covers the complete packet. Thus, any packet with residual bit errors is discarded. Speech codecs like AMR, however, can tolerate bit errors in less sensitive parts of the bitstream. A more recent development, UDP-lite, provides unequal error detection with a partial checksum that covers only the sensitive parts of a packet. Thus, only packets with errors in important bits are discarded. We compare the use of UDP-lite for UMTS channels with convolutional and channels with turbo coding. The results show that the achievable quality improvement by applying UDP-lite depends on the residual bit error distribution of the chosen UMTS channel coding method. While we determined a quality improvement for channels with convolutional coding, we did not get an improvement for turbo coding. Furthermore, when combined with header compression, the convolutional coder with use of UDP-lite can reach the performance of the turbo coder with use of UDP
Quality of augmented reality experience: A correlation analysis

We performed a user study in which we investigated the user experience of HoloBee, a visual analytics system for the exploration of bee drift data. We compared two user interfaces, a head-mounted augmented reality (aR) interface and a desktop interface. We present here a correlation analysis of the user experience ratings that we collected in a questionnaire: intuitive, easy to use, comfortable, natural, and efficient. All criteria are strongly correlated with an interface preference rating. Efficient and comfortable are, respectively, found to be most and least strongly correlated with interface preference. Furthermore, while all rating criteria are highly correlated amongst themselves for the desktop interface, natural and intuitive are only marginally correlated for the AR interface indicating that natural user interfaces are not necessarily intuitive.
Cognitive correlates of overt and covert attention in visual search

Human attention is an important cognitive resource to take into account when designing effective human-machine interaction and cognitive computing systems. Much of our knowledge about attention processing stems from search tasks that are usually framed around Treisman's feature integration theory. However, search performance in these tasks has mainly been investigated using an overt attention paradigm. Covert attention on the other hand has hardly been investigated in this context. To gain a more thorough understanding of human attentional processing, we have experimentally compared search performance when people are instructed to either overtly or covertly search for targets under a variety of target/distractor combinations. The overt search results presented in this work agree well with the guided search studies by Wolfe et al. While response times are similar between the two attention conditions, we found that error rates are considerably higher in covert search.
Collaborative Framework Design for Immersive Analytics

Recent trends in computing environments indicate that the future infrastructure for visual analytics will be distributed and collaborative. Collaborative frameworks create value for scientists, analysts, industrial partners, domain experts, and other end-users to meet, communicate, interact with others, and coordinate their activities in a globally shared network. This paper focuses on collaborative framework design for immersive analytics facilitating the integration of multimodal immersive interfaces. The framework design takes into account visualisation and interaction techniques for multiple users and especially decision support tools for scientific visual analytics experts. An overview of several important aspects of collaborative platforms for immersive analytics is presented and different modules of our proposed platform (including data management, analytics, visualisation, querying, and user interface design) will be detailed to highlight their importance in a full visual analytics pipeline.
Pareto optimalweighting of structural impairments for wireless imaging quality assessment

In this paper, we consider multiobjective optimization for obtaining the feature weights associated with structural image impairments and its application to wireless imaging quality assessment. The proposed framework supports optimization with respect to finding a trade-off between metric prediction accuracy and generalization to unknown images. Evaluation of optimal trade-off solutions for two representative scenarios reveal the benefits of the proposed approach. In particular, quality prediction accuracy of an objective image metric can be strongly increased and negligible features are identified that can be discarded to save computational complexity.
Visual and Manual Control for Human-Robot Teleoperation

In an effort to assess simple control modalities for a remote robotic system, this study explores and tests the suitability of four interfaces for teleoperation in human-robot interaction. For a pick-and-place task, users were asked to select targets and locations using eye tracking (activated by either a mouse click or dwell time), a touchscreen, or a standard computer mouse. Contrary to their expectations, the authors found that eye-tracking-based interaction, especially when paired with manual-click selection, was generally slower and was perceived as more difficult than the mouse and touchscreen interfaces. Conversely, as predicted, they found evidence that eye tracking with dwell selection was less prone to interference caused by a secondary manual task.