nils-herrmann/clean_scirepeval huggingface dataset:
Blind Image Deblurring Using Row–Column Sparse Representations

Blind image deblurring is a particularly challenging inverse problem where the blur kernel is unknown and must be estimated en route to recover the deblurred image. The problem is of strong practical relevance since many imaging devices, such as cellphone cameras, must rely on deblurring algorithms to yield satisfactory image quality. Despite significant research effort, handling large motions remains an open problem. In this letter, we develop a new method called blind image deblurring using row–column sparsity (BD-RCS) to address this issue. Specifically, we model the outer product of kernel and image coefficients in certain transformation domains as a rank-one matrix, and recover it by solving a rank minimization problem. Our central contribution then includes solving two new optimization problems involving RCS to automatically determine blur kernel and image support sequentially. The kernel and image can then be recovered through a singular value decomposition. Experimental results on linear motion deblurring demonstrate that BD-RCS can yield better results than state of the art, particularly when the blur is caused by large motion. This is confirmed both visually and through quantitative measures.
Adaptive sequential refinement: A tractable approach for ambiguity function shaping in cognitive radar

Ambiguity function shaping continues to be one of the most challenging open problems in cognitive radar. Analytically, a complex quartic function should be optimized as a function of the radar waveform code. Practical considerations further require that the waveform be constant modulus, which exacerbates the issue and leads to a hard non-convex problem. We develop a new approach called Adaptive Sequential Refinement (ASR) to suppress the clutter returns for a desired range-Doppler, i.e. ambiguity function response. ASR solves the aforementioned optimization problem in a unique iterative manner such that the formulation is updated depending on the iteration index. We establish formally that: 1.) the problem in each step of the iteration has a closed form solution, and 2.) monotonic decrease of the cost function until convergence is guaranteed. Experimental validation shows that ASR produces a radar waveform with higher Signal to Interference Ratio (SIR) and superior ambiguity function shaping than state of the art alternatives even as its computational burden is orders of magnitude lower.
DFDL: Discriminative feature-oriented dictionary learning for histopathological image classification

In histopathological image analysis, feature extraction for classification is a challenging task due to the diversity of histology features suitable for each problem as well as presence of rich geometrical structure. In this paper, we propose an automatic feature discovery framework for extracting discriminative class-specific features and present a low-complexity method for classification and disease grading in histopathology. Essentially, our Discriminative Feature-oriented Dictionary Learning (DFDL) method learns class-specific features which are suitable for representing samples from the same class while are poorly capable of representing samples from other classes. Experiments on three challenging real-world image databases: 1) histopathological images of intraductal breast lesions, 2) mammalian lung images provided by the Animal Diagnostics Lab (ADL) at Pennsylvania State University, and 3) brain tumor images from The Cancer Genome Atlas (TCGA) database, show the significance of DFDL model in a variety problems over state-of-the-art methods.
Learning a low-rank shared dictionary for object classification

Despite the fact that different objects possess distinct class-specific features, they also usually share common patterns. Inspired by this observation, we propose a novel method to explicitly and simultaneously learn a set of common patterns as well as class-specific features for classification. Our dictionary learning framework is hence characterized by both a shared dictionary and particular (class-specific) dictionaries. For the shared dictionary, we enforce a low-rank constraint, i.e. claim that its spanning subspace should have low dimension and the coefficients corresponding to this dictionary should be similar. For the particular dictionaries, we impose on them the well-known constraints stated in the Fisher discrimination dictionary learning (FDDL). Further, we propose a new fast and accurate algorithm to solve the sparse coding problems in the learning step, accelerating its convergence. The said algorithm could also be applied to FDDL and its extensions. Experimental results on widely used image databases establish the advantages of our method over state-of-the-art dictionary learning methods.
Desynchronization resilient video fingerprinting via randomized, low-rank tensor approximations

The problem of summarizing videos by short fingerprints or hashes has garnered significant attention recently. While traditional applications of video hashing lie in database search and content authentication, the emergence of websites such as YouTube and DailyMotion poses a challenging problem of anti-piracy video search. That is, hashes or fingerprints of an original video (provided to YouTube by the content owner) must be matched against those uploaded to YouTube by users to identify instances of “illegal” or undesirable uploads. Because the uploaded videos invariably differ from the original in their digital representation (owing to incidental or malicious distortions), robust video hashes are desired. In this paper, we model videos as order-3 tensors and use multilinear subspace projections, such as a reduced rank parallel factor analysis (PARAFAC) to construct video hashes. We observe that unlike most standard descriptors of video content, tensor based subspace projections can offer excellent robustness while effectively capturing the spatio-temporal essence of the video for discriminability. We further randomize the construction of the hash by dividing the video into randomly selected overlapping sub-cubes to prevent against intentional guessing and forgery. The most significant gains are seen for the difficult attacks of spatial (e.g. geometric) as well as temporal (random frame dropping) desynchronization. Experimental validation is provided in the form of ROC curves and we further perform detection-theoretic analysis which closely mimics empirically observed probability of error.
Learning based image segmentation of post-operative CT-images: A hydrocephalus case study

Accurate estimation of volumes for cerebrospinal fluid (CSF) and brain before and after surgery (pre-op and post-op) plays an important role in analyzing treatment for hydrocephalus. This in turn, relies upon segmentation of brain imagery into brain tissue and CSF. Segmentation of preop images is a relatively straightforward problem and has been well researched. However, segmenting post-op CT-scans becomes challenging due to distorted anatomy and subdural hematoma collections pressing on the brain. Most intensity and feature based segmentation methods fail to separate subdurals from brain and CSF as subdural geometry varies greatly across different patients and their intensity varies with time. We combat this problem by a learning approach that treats segmentation as supervised classification at the pixel level, i.e. a training set of CT scans with labeled pixel identities is employed. Inspired by sparsity constrained classification, our central contribution is a dictionary learning framework that learns class (segment) specific dictionaries that can efficiently represent test samples from the same class while poorly represent corresponding samples from other classes. Because discriminating features are discovered automatically, we call our method feature learning for image segmentation (FLIS). Experiments performed on infant CT brain images acquired from CURE children0s hospital of Uganda reveal the success of our method against existing alternatives.
Automatic target recognition using discriminative graphical models

Of recent interest in automatic target recognition (ATR) is the problem of combining the merits of multiple classifiers. This is commonly done by “fusing” the soft-outputs of several classifiers into making a single decision. We observe that the improvement in recognition rates afforded by these approaches is due to the complementary yet correlated information captured by different features/signal representations that these individual classifiers employ. We present the use of probabilistic graphical models in modeling and capturing feature dependencies that are crucial for target classification. In particular, we develop a two-stage target recognition framework that combines the merits of distinct and sparse signal representations with discriminatively learnt graphical models. The first stage designs multiple projections yielding M > 1 sparse representations, while the second stage models each individual representation using graphs and combines these initially disjoint and simple graphical models into a thicker probabilistic graphical model. Experimental results show that our approach outperforms state-of-the art target classification techniques in terms of recognition rates. The use of graphical models is particularly meritorious when feature dimensionality is high and training is limited - a commonly observed constraint in synthetic aperture radar (SAR) imagery based target recognition.
Deep learning based image super-resolution with coupled backpropagation

Recently deep learning methods have been applied to image super-resolution (SR). Typically, these approaches involve training a single convolutional neural network that is trained to perform resolution enhancement. We propose a new low-complexity but effective algorithm called Superresolution with Coupled Backpropagation (SR-CBP) which builds two Coupled Auto-encoder Networks (CAN), resp. the high-resolution (HR) and low-resolution (LR) networks, that capture the features of both high and low resolution images. The two networks in CAN have the ability to self-reconstruct its own input. Specifically, SR-CBP allows joint training of the LR and HR networks to have middle layer representations that agree for a pair of images (high-resolution image and its corresponding low-resolution version). For an LR input image, its middle layer representation obtained via the trained LR network can be fed into the HR network to generate the SR result. Preliminary experiments show that SR-CBP can produce better results than state of the art single image superresolution methods based on sparse representations. The memory and storage requirements of CAN are lesser than existing deep learning based SR methods.
Robust Extrema Features for Time-Series Data Analysis

The extraction of robust features for comparing and analyzing time series is a fundamentally important problem. Research efforts in this area encompass dimensionality reduction using popular signal analysis tools such as the discrete Fourier and wavelet transforms, various distance metrics, and the extraction of interest points from time series. Recently, extrema features for analysis of time-series data have assumed increasing significance because of their natural robustness under a variety of practical distortions, their economy of representation, and their computational benefits. Invariably, the process of encoding extrema features is preceded by filtering of the time series with an intuitively motivated filter (e.g., for smoothing), and subsequent thresholding to identify robust extrema. We define the properties of robustness, uniqueness, and cardinality as a means to identify the design choices available in each step of the feature generation process. Unlike existing methods, which utilize filters &#x201C;inspired&#x201D; from either domain knowledge or intuition, we explicitly optimize the filter based on training time series to optimize robustness of the extracted extrema features. We demonstrate further that the underlying filter optimization problem reduces to an eigenvalue problem and has a tractable solution. An encoding technique that enhances control over cardinality and uniqueness is also presented. Experimental results obtained for the problem of time series subsequence matching establish the merits of the proposed algorithm.
Compact Video Fingerprinting via Structural Graphical Models

Much previous work in video fingerprinting has focused on robustness and security issues, but the compactness requirement, i.e., the hash should be of a short length with acceptable robustness and discriminability, continues to be a significant practical challenge. In this paper, we propose a video fingerprinting method with explicit attention on compactness. First, we develop a new graphical representation of the video which reduces temporal redundancies and makes robust feature extraction much more economical. Second, a randomized adaptive quantizer is proposed to further decrease the final hash length while maintaining acceptable detection performance in terms of receiver operating characteristics (ROCs). Experimental results reveal that the proposed method offers a more favorable robustness versus discriminability tradeoff over the state of the art particularly when the bit budget of the video fingerprint is low.
Group structured dirty dictionary learning for classification

Dictionary learning techniques have gained tremendous success in many classification problems. Inspired by the dirty model for multi-task regression problems, we proposed a novel method called group-structured dirty dictionary learning (GDDL) that incorporates the group structure (for each task) with the dirty model (across tasks) in the dictionary training process. Its benefits are two-fold: 1) the group structure enforces implicitly the label consistency needed between dictionary atoms and training data for classification; and 2) for each class, the dirty model separates the sparse coefficients into ones with shared support and unique support, with the first set being more discriminative. We use proximal operators and block coordinate decent to solve the optimization problem. GDDL has been shown to give state-of-art result on both synthetic simulation and two face recognition datasets.
Computationally efficient toeplitz approximation of structured covariance under a rank constraint

Disturbance covariance estimation is a centrally important problem in radar space-time adaptive processing (STAP). Because training is invariably scarce, estimators that exploit inherent structure and physical radar constraints are needed in practice. This paper develops a new computationally efficient estimator that obtains a Toeplitz approximation of the structured interference covariance under a rank constraint. Previous work has shown that exact maximum likelihood (ML) estimation of Toeplitz covariance matrix has no closed-form solution, and most versions of this problem result in iterative estimators that are computationally expensive. Our proposed solution focuses on a computationally efficient approximation and involves a cascade of two closed-form solutions. First, we obtain the rank-constrained ML estimator whose merits have recently been established firmly for radar STAP. The central contribution of this paper is the rank-preserving Toeplitz approximation, which we demonstrate can be modeled as an equality-constrained quadratic program and also admits a closed form. Extensive performance evaluation on both simulated and knowledge-aided sensor signal processing and expert reasoning data confirms that the proposed estimator yields unbeatable performance for radar STAP under the previously stated conditions of rank and Toeplitz constraints.
Geometrically Invariant Image Watermarking via Robust Perceptual Hashes

We consider the problem of blind watermark detection from an image, when the image has undergone a geometric attack. Existing schemes for solving this problem involve insertion of periodic marks or templates, invariant properties of transforms, and feature point based techniques. A major deficiency of these approaches is either a security leak (owing to redundancy in the mark) and/or poor watermark detection performance esp. under lossy geometric attacks, e.g. cropping. In this paper, we introduce a novel watermark synchronization paradigm by using perceptual image hashes. We exploit the fact that a perceptual hash acts as a robust as well as randomized digest of image content, and use it to embed a synchronization mark into the image. Unlike existing synchronization schemes, our synchronization mark has favorable security and robustness to adversarial attacks and hence, cannot be easily removed. Experimental results confirm that our proposed algorithm has significantly lower watermark detection error probabilities than existing methods.
A Maximum a Posteriori Estimation Framework for Robust High Dynamic Range Video Synthesis

High dynamic range (HDR) image synthesis from multiple low dynamic range exposures continues to be actively researched. The extension to HDR video synthesis is a topic of significant current interest due to potential cost benefits. For HDR video, a stiff practical challenge presents itself in the form of accurate correspondence estimation of objects between video frames. In particular, loss of data resulting from poor exposures and varying intensity makes conventional optical flow methods highly inaccurate. We avoid exact correspondence estimation by proposing a statistical approach via maximum a posterior estimation, and under appropriate statistical assumptions and choice of priors and models, we reduce it to an optimization problem of solving for the foreground and background of the target frame. We obtain the background through rank minimization and estimate the foreground via a novel multiscale adaptive kernel regression technique, which implicitly captures local structure and temporal motion by solving an unconstrained optimization problem. Extensive experimental results on both real and synthetic data sets demonstrate that our algorithm is more capable of delivering high-quality HDR videos than current state-of-the-art methods, under both subjective and objective assessments. Furthermore, a thorough complexity analysis reveals that our algorithm achieves better complexity-performance tradeoff than conventional methods.
Power-Constrained RGB-to-RGBW Conversion for Emissive Displays: Optimization-Based Approaches

We propose an optimization-based power-constrained red-green-blue (RGB)-to-red-green-blue-white (RGBW) conversion algorithm for emissive RGBW displays. We measure the perceived color distortion using a color difference model in a perceptually uniform color space, and compute the power consumption for displaying an RGBW pixel on an emissive display. The central contribution of this paper is to formulate the optimization problem to minimize the color distortion subject to a constraint on the power consumption. Subsequently, we solve the optimization problem efficiently to convert an image in real time. Furthermore, based on the properties of the human visual system, we extend the proposed algorithm to image-dependent conversion that can preserve spatial detail in an input image. The simulation results show that the proposed algorithm provides a significantly less color distortion than the conventional methods, while providing a graceful tradeoff with the amount of power consumed. Specifically, it is shown that the power consumption can be reduced by up to 20%, while providing about 50% less color distortion than the conventional algorithms. In addition, a subjective evaluation on a real RGBW display is performed, which reveals the merits of the proposed image-dependent conversion for improving the perceptual quality over state-of-the-art techniques.
Expected likelihood approach for determining constraints in covariance estimation

Recent covariance estimation methods for radar space-time adaptive processing exploit practical constraints such as the rank of clutter subspace and the condition number of disturbance covariance to estimate accurate covariance even when training is not generous. While rank and condition number are very effective constraints, often practical nonidealities make it difficult to know them precisely using physical models. Therefore, we propose a method to determine constraints in covariance estimation for radar space-time adaptive processing via an expected likelihood approach. We analyze three cases of constraints: 1) a rank constraint, 2) both rank and noise power constraints, and 3) a condition number constraint. In each case, we formulate precise constraint determination as an optimization problem. For each of the three cases, we derive new analytical results which allow for computationally efficient, practical ways of determining these constraints with formal proofs. Through experimental results from a simulation model and the KASSPER data set, we show that the estimator with optimal constraints obtained by the expected likelihood approach outperforms state-of-the-art alternatives.
A joint sparsity model for video anomaly detection

Video anomaly detection can be used in the transportation domain to identify unusual patterns such as traffic violations, accidents, unsafe driver behavior, street crime, and other suspicious activities. A common class of approaches relies upon object tracking and trajectory analysis. A key challenge is the ability to effectively handle occlusions among objects and their trajectories. Another challenge is the detection of joint anomalies between multiple moving objects. Recently sparse reconstruction techniques have been used for image classification, and shown to provide excellent robustness to occlusion. This paper proposes a new joint sparsity model for anomaly detection that effectively addresses both the robustness to occlusion and the detection of joint anomalies involving multiple objects. Experimental results on real and synthetic data demonstrate the effectiveness of our approach for both single-object and multi-object anomalies.
On the practical merits of rank constrained ML estimator of structured covariance matrices

Estimation of the disturbance or interference covariance matrix plays a central role on radar target detection in the presence of clutter, noise and jammer. The disturbance covariance matrix should be inferred from training sample observations in practice. Traditional maximum likelihood (ML) estimators lead degraded false alarm and detection performance in the realistic regime of limited training. For this reason, informed estimators have been actively researched. Recently, a new estimator [1] that explicitly incorporates rank information of the clutter subspace was proposed. This paper reports significant new analytical and experimental investigations on the rank-constrained maximum likelihood (RCML) estimator. First, we show that the RCML estimation problem formulated in  has a closed form. Next, we perform new and rigorous experimental evaluation in the form of reporting: 1.) probability of detection versus signal to noise ratio (SNR), and 2.) SINR performance under heterogeneous (target corrupted) training data. In each case, we compare against widely used existing estimators and show that exploiting the rank information has significant practical merits in robust estimation.