nils-herrmann/clean_scirepeval huggingface dataset:
Noncontact Millimeter-Wave Real-Time Detection and Tracking of Heart Rate on an Ambulatory Subject

This paper presents a solution to an aiming problem in the remote sensing of vital signs using an integration of two systems. The problem is that to collect meaningful data with a millimeter-wave sensor, the antenna must be pointed very precisely at the subject's chest. Even small movements could make the data unreliable. To solve this problem, we attached a camera to the millimeter-wave antenna, and mounted this combined system on a pan/tilt base. Our algorithm initially finds a subject's face and then tracks him/her through subsequent frames, while calculating the position of the subject's chest. For each frame, the camera sends the location of the chest to the pan/tilt base, which rotates accordingly to make the antenna point at the subject's chest. This paper presents a system for concurrent tracking and data acquisition with results from some sample scenarios.
Defocus-invariant image registration for phase-difference detection auto focusing

This paper presents a defocus-invariant image registration method for measuring the shifting value between two differently located patterns in an imaging sensor. Existing registration methods fail with unfocused images since features or regions of interest are degraded by defocus. In order to solve this problem, the proposed method consists of three stages: i) pre-generation of the set of point spread functions (PSFs) estimated in different focusing positions, ii) the geometric transformation estimation using estimated PSF data, and iii) registration using estimated transformation matrix. The proposed method improves out-of-focus degradation through estimation of PSF. For this reason, the proposed method can accurately estimate the difference of phase between two out-of-focus images. Furthermore, it can be applied to phase-difference detection auto focusing, and provide accurate auto focusing performance.
Cross-validation and other criteria for estimating the regularizing parameter

The application of regularization to ill-conditioned problems necessitates the choice of a regularizing parameter which trades fidelity to the data for smoothness of the solution. Methods based on the properties of the residuals and on the generalized cross-validation have been proposed for estimating the regularizing parameter. Alternative methods to compute the regularizing parameter are proposed. The resulting values of the regularizing parameter are compared with the values obtained from the above-mentioned methods. Furthermore, it is shown that under certain conditions all the above-mentioned methods result in the same value for the regularizing parameter. Experimental results are presented which verify theoretical results.<<ETX>>
Regularized restoration of partial-response distortions in sporadically degraded images

The complex image degradations observed in (video) image sequences are often due to partial-response mechanisms caused by the physical limitations of practical cameras. The restoration of these distortions require more sophisticated image models that consider the underlying image acquisition process. In this work, a broad class of spatially varying distortions is defined. The observed image is modeled as the physically meaningful superposition of K partially degraded images. In areas where the degradation is due to the partial-response of the sensor to multiple object (regions), the distortion is said to be clustered or sporadic. The sporadic distortions under consideration here are spatially varying, object dependent and difficult to estimate from the observed data. A regularized iterative restoration of sporadically degraded images resulting in K restored images is proposed. The algorithm demonstrates a promising (object based) image restoration approach for video editing applications.
A new constraint for the regularized enhancement of compressed video

A novel fidelity constraint to the image enhancement problem is presented. With this constraint, we exploit the motion vectors of a compressed video bit-stream. These vectors establish a correspondence between image pixels across a series of frames, and we guarantee that processing the decoded sequence does not violate this correspondence. We develop the constraint within the context of MPEG-2 and incorporate the constraint into a regularized enhancement algorithm. Simulations are then performed. Quantitative and qualitative results illustrate an improvement in visual quality.
Model-based synthetic view generation from a monocular video sequence

In this paper a model-based multi-view image generation system for video conferencing is presented. The system assumes that a 3-D model of the person in front of the camera is available. It extracts texture from speaking person sequence images and maps it to the static 3-D model during the videoconference session. Since only the incrementally updated texture information is transmitted during the whole session, the bandwidth requirement is very small. Based on the experimental results one can conclude that the proposed system is very promising for practical applications.
Colorizing a Masterpiece [Applications Corner]

The purpose of this article is to discuss the process of colorizing a historical arti fact-a black and white archival photo of Bathers by a River, 1909-1917, by Henri Matisse (Art Institute of Chicago 1953.158), taken in November 1913, when the art ist was still working on the painting and showing it in a significantly differ ent state compared to the one seen today. Historical accounts describe a painting that was originally a more nat uralistic, pastoral image; but over the course of several years, and under the influence of Cubism and the circum stances of World War I, Matisse radical ly revised his monumental canvas (measuring 260 X 392 cm). Matisse later considered Bathers by a River to be one of the five most pivotal works of his career . Historical photographs unearthed by archival research depict the painting at various stages. The painting, along with these historical photographs, our colorized image, and other works documenting the experi mental nature of the artist's output during a period that has been studied very little until now, have been the cen terpiece of a recent exhibit, Matisse: Radical Invention 1913-1917, that was at the Art Institute of Chicago and the Museum of Modern Art in New York City in March-October 2010.
Pre- and post-processing algorithms for compressed video enhancement

Standards-based video compression algorithms are rapidly becoming the preferred method for transmitting image sequences. Prominent examples include the MPEG and ITU family of standards. However, it is important to realize that these standards are not bit-exact, in that only the operation of the decoder is defined by the specification. Development of the rate-control mechanism and pre- and post-processing procedures is completely controlled by the system designer, and these components can introduce discernible differences between two standards compliant realizations. We survey the fields of pre- and post-processing techniques for video compression. We then discuss our current work on compression enhancement algorithms. These algorithms are applicable to any compression standard but are discussed within the context of MPEG-2.
Joint source-channel coding and power allocation for video transmission over wireless fading channels

Techniques for modeling and simulating channel conditions play an essential role in efficient real-time video transmission over wireless networks. In this paper, we consider a Finite-State Markov Chain (FSMC) as the channel model to perform Joint Source Channel Coding with power allocation (JSCCPA). The optimization is done in an integrated manner and in one step. Computer simulations are performed to show the advantages of the proposed model.
Shape approximation through recursive scalable layer generation

This paper presents an efficient recursive algorithm for generating operationally optimal intra mode scalable layer decompositions of object contours. The problem is posed in terms of minimizing the shape distortion at full reconstruction subject to the total (for all scalable layers) bit budget constraint. Based on the chosen vertex-based representation, we solve the problem of determining the number and locations of approximating vertices for all scalable layers jointly and optimally. The number of scalable layers is not constrained, but, rather, is a by-product of the proposed optimization. The algorithm employs two different coding strategies: one for the base layer and one for the enhancement layers. By carefully defining scalable layer recursion and base layer segment costs the problem is solved by executing a directed acyclic graph (DAG) shortest path algorithm.
Multiple input adaptive iterative image restoration algorithms

In this paper image restoration applications where multiple distorted versions of the same original image are available, are considered. A general adaptive iterative restoration algorithm is derived based on regularization techniques. The adaptivity of the algorithm is introduced in two ways: a) by a constraint operator which incorporates properties of the response of the human visual system into the restoration process, and b) by a weight matrix which assigns greater importance for the deconvolution process to areas of high spatial activity than to areas of low spatial activity. Different degrees of trust are assigned to the various distorted images depending on the amount of noise on each image. The proposed algorithms are general and can be used for any type of linear distortion and constraint operators. It can also be used to restore signals other than images.
Joint Data Detection and Channel Tracking for OFDM Systems With Phase Noise

This paper addresses the problem of data detection in orthogonal frequency division multiplexing (OFDM) systems operating under a time-varying multipath fading channel. Optimal detection in such a scenario is infeasible, which makes the introduction of approximations necessary. The typical joint data-channel estimators are decision directed, that is, assume perfect past data decisions. However, their performance is subject to error propagation phenomena. The variational Bayes method is employed here, which approximates the joint data and channel distribution as a separable one, greatly simplifying the problem. The data detection part of the resulting algorithm provides soft data estimates that are used for channel tracking. The channel itself is modeled as an autoregressive process allowing for a Kalman-like tracking algorithm. According to the developed algorithm, both data and channel estimates are exchanged and updated in an iterative manner. The performance of the proposed algorithm is evaluated by simulations. Furthermore, since OFDM is extremely sensitive to the presence of phase noise, the algorithm is extended to operate under severe phase noise conditions, with moderate performance degradation.
Resolution enhancement of monochrome and color video using motion compensation

We propose an iterative algorithm for enhancing the resolution of monochrome and color image sequences. Various approaches toward motion estimation are investigated and compared. Improving the spatial resolution of an image sequence critically depends upon the accuracy of the motion estimator. The problem is complicated by the fact that the motion field is prone to significant errors since the original high-resolution images are not available. Improved motion estimates may be obtained by using a more robust and accurate motion estimator, such as a pel-recursive scheme instead of block matching, in processing color image sequences, there is the added advantage of having more flexibility in how the final motion estimates are obtained, and further improvement in the accuracy of the motion field is therefore possible. This is because there are three different intensity fields (channels) conveying the same motion information. In this paper, the choice of which motion estimator to use versus how the final estimates are obtained is weighed to see which issue is more critical in improving the estimated high-resolution sequences. Toward this end, an iterative algorithm is proposed, and two sets of experiments are presented. First, several different experiments using the same motion estimator but three different data fusion approaches to merge the individual motion fields were performed. Second, estimated high-resolution images using the block matching estimator were compared to those obtained by employing a pel-recursive scheme. Experiments were performed on a real color image sequence, and performance was measured by the peak signal to noise ratio (PSNR).
Fast iteratively reweighted least squares for lp regularized image deconvolution and reconstruction

Iteratively reweighted least squares (IRLS) is one of the most effective methods to minimize the lp regularized linear inverse problem. Unfortunately, the regularizer is nonsmooth and nonconvex when 0 <; p <; 1. In spite of its properties and mainly due to its high computation cost, IRLS is not widely used in image deconvolution and reconstruction. In this paper, we first derive the IRLS method from the perspective of majorization minimization and then propose an Alternating Direction Method of Multipliers (ADMM) to solve the reweighted linear equations. Interestingly, the resulting algorithm has a shrinkage operator that pushes each component to zero in a multiplicative fashion. Experimental results on both image deconvolution and reconstruction demonstrate that the proposed method outperforms state-of-the-art algorithms in terms of speed and recovery quality.
New Results on Efficient Optimal Multilevel Image Thresholding

Image thresholding is one of the most common image processing operations, since almost all image processing schemes need some sort of separation of the pixels into different classes. In order to find the thresholds, almost all methods analyze the histogram of the image. In most cases, the optimal thresholds are found by either minimizing or maximizing an objective function, which depends on the positions of the thresholds. We identify two classes of objective functions for which the optimal thresholds can be found by algorithms with low time complexity. We show, that for example the method proposed by Otsu (1979) and other well known methods have objective functions belonging to these classes. By implementing the algorithms in ANSI C and comparing their execution times, we can make a quantitative statement about their performance.
On video SNR scalability

We compare two SNR scalable video codecs. The first codec (CODEC1) is a three-layer single-pass quantization algorithm based on H.263 and extends the work presented by Robers (see Master's Thesis Northwestern University, 1997) and Robers, Kondi and Katsaggelos ). The second codec (CODEC2) implements three layer SNR scalability as described in Annex O of the H.263 standard by requantizing the DCT coefficients of the encoding error using finer quantizers than those used in the previous layer. By testing the two algorithms at various base and enhancement layer rates on both high and low motion sequences, the advantages and disadvantages of each codec are discussed and suitable applications are suggested for each one.
Decision-aided compensation of severe phase-impairment-induced inter-carrier interference in frequency-selective OFDM

A new, reduced complexity algorithm is proposed for compensating the Inter-Carrier Interference (ICI) caused by severe PHase Noise (PHN) and Residual Frequency Offset (RFO) in OFDM systems. The algorithm estimates and compensates the most significant terms of the frequency domain ICI process, which are optimally selected via a Minimum Mean Squared Error (MMSE) criterion. The algorithm requires minimal knowledge of the phase process statistics, the estimation of which is also considered. The scheme outperforms previously proposed compensation methods of similar complexity, when severe phase impairments are present.
Construction of Incoherent Unit Norm Tight Frames With Application to Compressed Sensing

Despite the important properties of unit norm tight frames (UNTFs) and equiangular tight frames (ETFs), their construction has been proven extremely difficult. The few known techniques produce only a small number of such frames while imposing certain restrictions on frame dimensions. Motivated by the application of incoherent tight frames in compressed sensing (CS), we propose a methodology to construct incoherent UNTFs. When frame redundancy is not very high, the achieved maximal column correlation becomes close to the lowest possible bound. The proposed methodology may construct frames of any dimensions. The obtained frames are employed in CS to produce optimized projection matrices. Experimental results show that the proposed optimization technique improves CS signal recovery, increasing the reconstruction accuracy. Considering that the UNTFs and ETFs are important in sparse representations, channel coding, and communications, we expect that the proposed construction will be useful in other applications, besides the CS.
Edge detection using a neural network

An edge detection algorithm using multistate ADALINES (adaptive linear neurons) is presented. The proposed algorithm can suppress noise effects without increasing the mask size. The input states are defined using the local mean in a predefined mask, and the one-dimensional edges are defined so that they are linearly separable from nonedges. The two-dimensional edges are obtained using the rotation invariant property of layered neural networks. The proposed algorithm requires much less computation compared with Marr and Hildreth's (1980) edge detector for similar performance. An application of the proposed edge detector to adaptive image restoration is also presented.<<ETX>>
Scale hyperparameter estimation for GGMRF prior models with application to SPECT images

In this work we develop a Bayesian reconstruction method for SPECT (single photon emission computed tomography) images, using as prior GGMRF (generalized Gaussian Markov random fields) distributions and estimating the scale hyperparameter following the evidence analysis. Preconditioning methods are used to estimate this hyperparameter and the approximations used are compared on synthetic images.
Resolution enhancement of video sequences using motion compensation

Improving the spatial resolution of an image sequence critically depends upon the accuracy of the motion estimator. The problem is complicated by the fact that the motion field is prone to significant errors since original high resolution images are not available. In earlier work, bilinearly interpolated images were used as initial conditions for the proposed algorithm. In this paper, the use of other initial conditions, such as previously estimated images, is experimentally investigated. Furthermore, various forms of the iterative video resolution enhancement algorithm are studied experimentally.
Blind image restoration using local bound constraints

A new method of incorporating local image characteristics into blind image restoration is proposed. The local variance of the degraded image is used as a measure of spatial activity, from which individual pixel bounds are determined. A parameter defined by the user controls the degree of smoothing. The local bounds define the solution more precisely than smoothness constraints on the image (including those that are spatially-adaptive), reducing the number of possible solutions and leading to a faster rate of convergence. Experimental results demonstrate the potential of this method as an alternative/supplement to smoothing constraints in blind image restoration.
Tracking-optimal pre- and post-processing for H.264 compression in traffic video surveillance applications

The compression of video can reduce the accuracy of automated tracking algorithms. This is problematic for centralized applications such as transportation surveillance systems, where remotely captured and compressed video is transmitted to a central location for tracking. In typical systems, the majority of communications bandwidth is spent on representing events such as capture noise or local changes to lighting. We propose a pre- and post-processing algorithm that identifies and removes such events of low tracking interest, significantly reducing the bitrate required to transmit remotely captured video while maintaining comparable tracking accuracy. Using the H.264/AVC video coding standard and a commonly used state-of-the-art tracker we show that our algorithm allows for up to 90% bitrate savings while maintaining comparable tracking accuracy.
Restoration of single-channel images with multi-channel filtering in the wavelet domain

The authors develop a multi-channel formulation of a single-channel filtering problem in the wavelet domain. This technique is based on an orthogonal matrix representation of the 2-D separable wavelet transform. It is shown that semiblock circulant structures resulting from a similarity transformation with this matrix operator permit a multi-channel treatment of a noisy blurred image, yielding an adaptive restoration technique in the wavelet frequency domain. A novel formulation of the 2-D separable wavelet decomposition is presented that clearly produces matrices which may be block-diagonalized with an array Fourier transform. Some experimental results based on the implementation of the wavelet-based multi-channel filtering methods discussed are presented. The method described offers much potential for improved adaptive image restoration in the wavelet domain.<<ETX>>
Video Super-Resolution With Convolutional Neural Networks

Convolutional neural networks (CNN) are a special type of deep neural networks (DNN). They have so far been successfully applied to image super-resolution (SR) as well as other image restoration tasks. In this paper, we consider the problem of video super-resolution. We propose a CNN that is trained on both the spatial and the temporal dimensions of videos to enhance their spatial resolution. Consecutive frames are motion compensated and used as input to a CNN that provides super-resolved video frames as output. We investigate different options of combining the video frames within one CNN architecture. While large image databases are available to train deep neural networks, it is more challenging to create a large video database of sufficient quality to train neural nets for video restoration. We show that by using images to pretrain our model, a relatively small video database is sufficient for the training of our model to achieve and even improve upon the current state-of-the-art. We compare our proposed approach to current video as well as image SR algorithms.
Speckle noise reduction and segmentation on polarization sensitive optical coherence tomography images

The retinal layers of a monkey were imaged using a polarization sensitive optical coherence tomography (PS-OCT) system in an effort to develop a clinically reliable automatic diagnostic system for glaucoma. Glaucoma is characterized by the progressive loss of ganglion cells and axons in the retinal nerve fiber layer (RNFL). Automatic segmentation of the RNFL from the PS-OCT images is a fundamental step to diagnose the progress of the disease. Due to the use of a coherent light, speckle noise is inherent in the images. Wavelet denoising techniques with a combination of image processing techniques were applied to remove the speckle noise in the PS-OCT images, and a fuzzy logic classifier was used to segment the RNFL. A significant signal to noise ratio improvement was observed qualitatively and quantitatively after the denoising. The upper boundary for the RNFL was reliably detected, but the lower boundary detection still remains as a problem.