nils-herrmann/clean_scirepeval huggingface dataset:
Compressive Light Field Sensing

We propose a novel design for light field image acquisition based on compressive sensing principles. By placing a randomly coded mask at the aperture of a camera, incoherent measurements of the light passing through different parts of the lens are encoded in the captured images. Each captured image is a random linear combination of different angular views of a scene. The encoded images are then used to recover the original light field image via a novel Bayesian reconstruction algorithm. Using the principles of compressive sensing, we show that light field images with a large number of angular views can be recovered from only a few acquisitions. Moreover, the proposed acquisition and recovery method provides light field images with high spatial resolution and signal-to-noise-ratio, and therefore is not affected by limitations common to existing light field camera designs. We present a prototype camera design based on the proposed framework by modifying a regular digital camera. Finally, we demonstrate the effectiveness of the proposed system using experimental results with both synthetic and real images.
Compressive passive millimeter-wave imaging

In this paper, we present a novel passive millimeter-wave (PMMW) imaging system designed using compressive sensing principles. We employ randomly encoded masks at the focal plane of the PMMW imager to acquire incoherent measurements of the imaged scene. We develop a Bayesian reconstruction algorithm to estimate the original image from these measurements, where the sparsity inherent to typical PMMW images is efficiently exploited. Comparisons with other existing reconstruction methods show that the proposed reconstruction algorithm provides higher quality image estimates. Finally, we demonstrate with simulations using real PMMW images that the imaging duration can be dramatically reduced by acquiring only a few measurements compared to the size of the image.
Combination of MR surface coil images using weighted constrained least squares

For most MR imaging applications multiple surface coils are used to obtain images with high signal-to-noise ratios (SNR). However, signal intensity strongly diminishes with distance. Although there are a number of approaches to combine the surface coil images to obtain a high SNR and bias-free image, most of them are developed in an ad hoc manner and lack a systematic treatment. In this work we propose a new approach, an iterative weighted constrained least squares (WCLS) restoration method, for combining surface coil images. The algorithm is fully automated and outperforms approaches which appeared in the literature.
Sparse Bayesian Methods for Low-Rank Matrix Estimation

Recovery of low-rank matrices has recently seen significant activity in many areas of science and engineering, motivated by recent theoretical results for exact reconstruction guarantees and interesting practical applications. In this paper, we present novel recovery algorithms for estimating low-rank matrices in matrix completion and robust principal component analysis based on sparse Bayesian learning (SBL) principles. Starting from a matrix factorization formulation and enforcing the low-rank constraint in the estimates as a sparsity constraint, we develop an approach that is very effective in determining the correct rank while providing high recovery performance. We provide connections with existing methods in other similar problems and empirical results and comparisons with current state-of-the-art methods that illustrate the effectiveness of this approach.
Bayesian compressive sensing of wavelet coefficients using multiscale Laplacian priors

In this paper, we propose a novel algorithm for image reconstruction from compressive measurements of wavelet coefficients. By incorporating independent Laplace priors on separate wavelet sub-bands, the inhomogeneity of wavelet coefficient distributions and therefore the structural sparsity within images are modeled effectively. We model the problem by adopting a Bayesian formulation, and develop a fast greedy reconstruction algorithm. Experimental results demonstrate that the reconstruction performance of the proposed algorithm is competitive with state-of-the-art methods while outperforming them in terms of running times.
Denoising diffusion-weighted MR magnitude image sequences using low rank and edge constraints

This paper addresses the denoising problem associated with diffusion MR imaging. Building on previous approaches to this problem, this paper presents a new method for joint denoising of a sequence of diffusion-weighted (DW) magnitude images. The proposed method uses a maximum a posteriori (MAP) estimation formulation to incorporate a Rician likelihood (for modeling the noisy magnitude data), a low rank model (for the DW image sequences) and a spatial prior (for imposing joint edge constraints). An efficient algorithm to solve the associated optimization problem is also described. The proposed method has been evaluated using both simulated and experimental diffusion tensor imaging (DTI) data, which yields very encouraging results both qualitatively and quantitatively.
Symmetrical EEG/FMRI fusion with spatially adaptive priors using variational distribution approximation

In this paper, we propose a symmetrical EEG/fMRI fusion algorithm which combines EEG and fMRI by means of a common generative model. The use of a total variation (TV) prior as well as spatially adaptive temporal priors enables adaptation to the local characteristics of the estimated responses. We utilize an approximate variational Bayesian framework and obtain a fully automatic fusion algorithm. Simulation results demonstrate that the proposed algorithm outperforms existing EEG/fMRI fusion methods.
Variational Bayesian Blind Deconvolution Using a Total Variation Prior

In this paper, we present novel algorithms for total variation (TV) based blind deconvolution and parameter estimation utilizing a variational framework. Using a hierarchical Bayesian model, the unknown image, blur, and hyperparameters for the image, blur, and noise priors are estimated simultaneously. A variational inference approach is utilized so that approximations of the posterior distributions of the unknowns are obtained, thus providing a measure of the uncertainty of the estimates. Experimental results demonstrate that the proposed approaches provide higher restoration performance than non-TV-based methods without any assumptions about the unknown hyperparameters.
Bayesian blind deconvolution from differently exposed image pairs

Photographs acquired under low-light conditions require long exposure times and therefore exhibit significant blurring due to the shaking of the camera. Using shorter exposure times results in sharper images but with a very high level of noise. In this paper we address this problem and present a novel blind deconvolution algorithm for a pair of differently exposed images. We formulate the problem in a hierarchical Bayesian framework by utilizing prior knowledge on the unknown image and blur, and also on the dependency between two observed images. By incorporating a fully Bayesian analysis, the developed algorithm estimates all necessary algorithm parameters along with the unknowns, such that no user-intervention is needed. Moreover, we employ a variational Bayesian inference procedure, which allows for the statistical compensation of errors occurring at different stages of the restoration, and also provides uncertainties of the estimates. Experimental results demonstrate the high restoration performance of the proposed algorithm.
Generalized Gaussian Markov random field image restoration using variational distribution approximation

In this paper we propose novel algorithms for image restoration and parameter estimation with a generalized Gaussian Markov random field (GGMRF) prior utilizing variational distribution approximation. The restored image and the unknown hyperparameters for both the image prior and the image degradation noise are simultaneously estimated within a hierarchical Bayesian framework. We develop two algorithms resulting from this formulation which provide approximations to the posterior distributions of the latent variables. Experimental results are provided to demonstrate the performance of the algorithms.
Maximum a posteriori super-resolution of compressed video using a new multichannel image prior

Super-resolution (SR) algorithms for compressed video aim at recovering high-frequency information and estimating a high-resolution (HR) image or a set of HR images from a sequence of low-resolution (LR) video frames. In this paper we present a novel SR algorithm for compressed video based on the maximum a posteriori (MAP) framework. We utilize a new multichannel image prior model, along with the state-of-the art image prior and observation models. Moreover, relationship between model parameters and the decoded bitstream are established. Numerical experiments demonstrate the improved performance of the proposed method compared to existing algorithms for different compression ratios.
Image restoration by mixture modelling of an overcomplete linear representation

We present a new image restoration method based on modelling the coefficients of an overcomplete wavelet response to natural images with a mixture of two Gaussian distributions, having non-zero and zero mean respectively, and reflecting the assumption that this response is close to be sparse. Including the observation model, the resulting procedure iterates between image reconstruction from the hard-thresholding of the response to the current estimate and a fast blur compensation step. Results indicate that our method compares favorably with current wavelet-based restoration methods.
Spatiotemporal Algorithm for Background Subtraction

and subtraction is a fundamental task in many computer vision and video processing applications. We present a novel probabilistic background modeling and subtraction method that exploits spatial and temporal dependencies between pixels. By using an initial clustering of the background scene, we model each pixel by a mixture of spatiotemporal Gaussian distributions, where each distribution represents locally a region in the neighborhood of the pixel. By extracting the local properties around each pixel, the proposed method obtains accurate models of dynamic backgrounds that are highly effective in detecting foreground objects. Experimental results for indoor and outdoor surveillance videos in comparison with other multimodal methods demonstrate the performance advantages of the proposed method.
Variational Bayesian Super Resolution

In this paper, we address the super resolution (SR) problem from a set of degraded low resolution (LR) images to obtain a high resolution (HR) image. Accurate estimation of the sub-pixel motion between the LR images significantly affects the performance of the reconstructed HR image. In this paper, we propose novel super resolution methods where the HR image and the motion parameters are estimated simultaneously. Utilizing a Bayesian formulation, we model the unknown HR image, the acquisition process, the motion parameters and the unknown model parameters in a stochastic sense. Employing a variational Bayesian analysis, we develop two novel algorithms which jointly estimate the distributions of all unknowns. The proposed framework has the following advantages: 1) Through the incorporation of uncertainty of the estimates, the algorithms prevent the propagation of errors between the estimates of the various unknowns; 2) the algorithms are robust to errors in the estimation of the motion parameters; and 3) using a fully Bayesian formulation, the developed algorithms simultaneously estimate all algorithmic parameters along with the HR image and motion parameters, and therefore they are fully-automated and do not require parameter tuning. We also show that the proposed motion estimation method is a stochastic generalization of the classical Lucas-Kanade registration algorithm. Experimental results demonstrate that the proposed approaches are very effective and compare favorably to state-of-the-art SR algorithms.
Bayesian Blind Deconvolution From Differently Exposed Image Pairs

Photographs acquired under low-lighting conditions require long exposure times and therefore exhibit significant blurring due to the shaking of the camera. Using shorter exposure times results in sharper images but with a very high level of noise. In this paper, we address the problem of utilizing two such images in order to obtain an estimate of the original scene and present a novel blind deconvolution algorithm for solving it. We formulate the problem in a hierarchical Bayesian framework by utilizing prior knowledge on the unknown image and blur, and also on the dependency between the two observed images. By incorporating a fully Bayesian analysis, the developed algorithm estimates all necessary model parameters along with the unknown image and blur, such that no user-intervention is needed. Moreover, we employ a variational Bayesian inference procedure, which allows for the statistical compensation of errors occurring at different stages of the restoration, and also provides uncertainties of the estimates. Experimental results with synthetic and real images demonstrate that the proposed method provides very high quality restoration results and compares favorably to existing methods even though no user supervision is needed.
Image denoising via bidirectional low rank representation with cluster adaptive dictionary

In this study, the authors propose a new image representation model that fully exploits the similarity inherent in natural images. The idea is based on an observation of similar patches. For a clean image, when a cluster of similar patches are collected to form the similar patch matrix (SPM), there exists high correlation among columns/rows of the SPM. This implies that, when the column/row vectors are linearly expressed by a dictionary, their coefficients of columns/rows should have high correlation, which leads to the coefficient matrixes of column/row representation are low-ranked. This observation inspires them to propose a novel image denoising model named bidirectional low rank representation (BiLRR) with cluster adaptive dictionary. Specifically, they use low rank penalties simultaneously on the coefficient matrixes of column and row representations to recover the correlation structure of the SPM. Meanwhile, a cluster adaptive dictionary is learned to represent each SPM so as to well preserve the fine structure of image. By applying variable splitting and penalty technique, they present an efficient alternative minimisation algorithm to solve the proposed BiLRR model. Experimental results indicate the authors’ method achieves a competitive denoising performance in comparison with state-of-the-art algorithms in terms of subjective and objective qualities.
Cross-Domain Visual Matching via Generalized Similarity Measure and Feature Learning

Cross-domain visual data matching is one of the fundamental problems in many real-world vision tasks, e.g., matching persons across ID photos and surveillance videos. Conventional approaches to this problem usually involves two steps: i) projecting samples from different domains into a common space, and ii) computing (dis-)similarity in this space based on a certain distance. In this paper, we present a novel pairwise similarity measure that advances existing models by i) expanding traditional linear projections into affine transformations and ii) fusing affine Mahalanobis distance and Cosine similarity by a data-driven combination. Moreover, we unify our similarity measure with feature representation learning via deep convolutional neural networks. Specifically, we incorporate the similarity measure matrix into the deep architecture, enabling an end-to-end way of model optimization. We extensively evaluate our generalized similarity model in several challenging cross-domain matching tasks: person re-identification under different views and face verification over different modalities (i.e., faces from still images and videos, older and younger faces, and sketch and photo portraits). The experimental results demonstrate superior performance of our model over other state-of-the-art methods.
Convolutional Sparse Coding for Image Super-Resolution

Most of the previous sparse coding (SC) based super resolution (SR) methods partition the image into overlapped patches, and process each patch separately. These methods, however, ignore the consistency of pixels in overlapped patches, which is a strong constraint for image reconstruction. In this paper, we propose a convolutional sparse coding (CSC) based SR (CSC-SR) method to address the consistency issue. Our CSC-SR involves three groups of parameters to be learned: (i) a set of filters to decompose the low resolution (LR) image into LR sparse feature maps, (ii) a mapping function to predict the high resolution (HR) feature maps from the LR ones, and (iii) a set of filters to reconstruct the HR images from the predicted HR feature maps via simple convolution operations. By working directly on the whole image, the proposed CSC-SR algorithm does not need to divide the image into overlapped patches, and can exploit the image global correlation to produce more robust reconstruction of image local structures. Experimental results clearly validate the advantages of CSC over patch based SC in SR application. Compared with state-of-the-art SR methods, the proposed CSC-SR method achieves highly competitive PSNR results, while demonstrating better edge and texture preservation performance.
Multiple wavelet basis image denoising by Wiener filter and projective method

We proposed a new image denoising algorithm that exploits an image's representation in multiple wavelet domains. In the proposed method, local Wiener filter is used to shrink noisy image wavelet coefficient, while the trained parameter is used to effectively combine estimates from different wavelet domains. The proposed algorithm provides an improved noise suppression performance as compared to the relevant algorithms using the two-dimensional separable wavelets.
Image decomposition and staircase effect reduction based on total generalized variation

Total variation (TV) is widely applied in image processing. The assumption of TV is that an image consists of piecewise constants, however, it suffers from the so-called staircase effect. In order to reduce the staircase effect and preserve the edges when textures of image are extracted, a new image decomposition model is proposed in this paper. The proposed model is based on the total generalized variation method which involves and balances the higher order of the structure. We also derive a numerical algorithm based on a primal-dual formulation that can be effectively implemented. Numerical experiments show that the proposed method can achieve a better trade-off between noise removal and texture extraction, while avoiding the staircase effect efficiently.
Image Denoising Using Low-Rank Dictionary and Sparse Representation

In this paper, we propose an image denoising model by using low-rank dictionary and sparse representation (LRSR). The K-SVD algorithm learns a universal dictionary for all patches in an image and the NLM exploits similarities of nonlocal patches, both achieve effective denoising performance. Motivated by these methods, we propose to use a low-rank dictionary for each cluster of similar patches and the dictionary is used to simultaneously produce sparse representations of all patches in the cluster. Our algorithm has two advantages. The first one is, we use a dictionary particular to each cluster of similar patches so that the dictionary can exploit the peculiar structure underlying the cluster and better adapts to the cluster. The second, we represent the similar patches in a cluster simultaneously by the dictionary so that we can impose a structured sparsity to make full use of similarities of these patches and get better restoration quality. Experimental results show that our method performs better than or on par with the state-of-the-art denoising methods such as BM3D and TDNL.
Simultaneous cartoon and texture for nonconvex image inpainting via the balanced approach

This paper describes an image inpainting algorithm based on two tight frame systems that can sparsely represent cartoons and textures respectively. The proposed minimization formulation adopts nonconvex approximation via balanced approach, and an iterative algorithm is derived to find its solution. Numerical simulations demonstrate that the proposed nonconvex algorithm can significantly improve the image inpainting quality over the usual l1 algorithm.
Weighted Nuclear Norm Minimization with Application to Image Denoising

As a convex relaxation of the low rank matrix factorization problem, the nuclear norm minimization has been attracting significant research interest in recent years. The standard nuclear norm minimization regularizes each singular value equally to pursue the convexity of the objective function. However, this greatly restricts its capability and flexibility in dealing with many practical problems (e.g., denoising), where the singular values have clear physical meanings and should be treated differently. In this paper we study the weighted nuclear norm minimization (WNNM) problem, where the singular values are assigned different weights. The solutions of the WNNM problem are analyzed under different weighting conditions. We then apply the proposed WNNM algorithm to image denoising by exploiting the image nonlocal self-similarity. Experimental results clearly show that the proposed WNNM algorithm outperforms many state-of-the-art denoising algorithms such as BM3D in terms of both quantitative measure and visual perception quality.
Blind Image Quality Assessment Using Joint Statistics of Gradient Magnitude and Laplacian Features

Blind image quality assessment (BIQA) aims to evaluate the perceptual quality of a distorted image without information regarding its reference image. Existing BIQA models usually predict the image quality by analyzing the image statistics in some transformed domain, e.g., in the discrete cosine transform domain or wavelet domain. Though great progress has been made in recent years, BIQA is still a very challenging task due to the lack of a reference image. Considering that image local contrast features convey important structural information that is closely related to image perceptual quality, we propose a novel BIQA model that utilizes the joint statistics of two types of commonly used local contrast features: 1) the gradient magnitude (GM) map and 2) the Laplacian of Gaussian (LOG) response. We employ an adaptive procedure to jointly normalize the GM and LOG features, and show that the joint statistics of normalized GM and LOG features have desirable properties for the BIQA task. The proposed model is extensively evaluated on three large-scale benchmark databases, and shown to deliver highly competitive performance with state-of-the-art BIQA models, as well as with some well-known full reference image quality assessment models.