nils-herrmann/clean_scirepeval huggingface dataset:
Optimizing image quality in MRI: On the evaluation of k-space trajectories for under-sampled MR acquisition

Modern magnetic resonance (MR) applications require high-speed acquisitions. One of the possible ways to accelerate the process is to acquire the data along a k-space trajectory at sub-Nyquist rate and then reconstruct the image by an iterative non-linear reconstruction algorithm. The choice of k-space trajectory and its parameters has a large influence on the image quality. For physicians it is more important to optimize the reconstructed image and thus the trajectory for diagnostic tasks than creating aesthetically pleasing images. Task-specific model observers have been proposed in order to replace the time-consuming and costly human observer experiments. Very recently, we have developed a novel model observer for signal-known-statistically tasks, which can also measure several image quality factors such as noise, blur and contrast without reference images. In this paper, we discuss the image quality for several k-space trajectories in a pilot study. We find that traditionally used measures such as RMSE or PSNR do not correlate with the diagnostic image quality. Alternative measures are brought through our newly developed model observers.
Fuzzy modeling of knowledge for MRI brain structure segmentation

In this paper, we propose a novel automatic method based on fuzzy modeling of knowledge to segment brain structures in MRI (magnetic resonance imaging) images. The segmentation is achieved by the region-wise classification using GAs (genetic algorithms), followed by voxel-wise refinement using parallel region growing. To improve the accuracy of the labeling, we introduce a fuzzy model of ROI (regions of interest) by analogy with the electrostatic potential distribution, to represent more appropriately knowledge of shape, distance and reaction between structures, and to estimate more reliably the statistical moments. This modeling is also used in the design of the fitness function of GAs, and the criteria of region growing. The performance of our proposed method is quantitatively validated by 4 indexes with respect to manually segmented images.
On the accuracy of 3D landscapes from UAV image data

Our surroundings change all the time. Applications that require 3D models of a changing terrain, such as urban planning, are becoming ever more demanding with respect to the cost to create them and the accuracy of the result. A novel, cheap and fast solution for this problem is given by a UAV to take aerial images of the terrain in question, in combination with structure from motion algorithms to create a 3D model from those aerial images. However the question remains whether these on-the-fly 3D maps can match the accuracy of classical surveyor based models, which require more time to create. In this paper we investigate this question, and find that under certain conditions the accuracy of the UAV based model matches the accuracy of surveyor generated measurements.
Combined wavelet domain and temporal video denoising

We develop a new filter which combines spatially adaptive noise filtering in the wavelet domain and temporal filtering in the signal domain. For spatial filtering, we propose a new wavelet shrinkage method, which estimates how probable it is that a wavelet coefficient represents a "signal of interest" given its value, given the locally averaged coefficient magnitude and given the global subband statistics. The temporal filter combines a motion detector and recursive time-averaging. The results show that this combination outperforms single resolution spatio-temporal filters in terms of quantitative performance measures as well as in terms of visual quality. Even though our current implementation of the new filter does not allow real-time processing, we believe that its optimized software implementation could be used for real- or near real-time filtering.
Image Denoising Using Mixtures of Projected Gaussian Scale Mixtures

We propose a new statistical model for image restoration in which neighborhoods of wavelet subbands are modeled by a discrete mixture of linear projected Gaussian scale mixtures (MPGSM). In each projection, a lower dimensional approximation of the local neighborhood is obtained, thereby modeling the strongest correlations in that neighborhood. The model is a generalization of the recently developed Mixture of GSM (MGSM) model, that offers a significant improvement both in PSNR and visually compared to the current state-of-the-art wavelet techniques. However, the computation cost is very high which hampers its use for practical purposes. We present a fast EM algorithm that takes advantage of the projection bases to speed up the algorithm. The results show that, when projecting on a fixed data-independent basis, even computational advantages with a limited loss of PSNR can be obtained with respect to the BLS-GSM denoising method, while data-dependent bases of Principle Components offer a higher denoising performance, both visually and in PSNR compared to the current wavelet-based state-of-the-art denoising methods.
Channelized Hotelling observers for signal detection in stack-mode reading of volumetric images on medical displays with slow response time

Volumetric medical images are commonly read in stack-browsing mode. However, previous studies suggest that slow temporal response of medical liquid crystal displays may degrade the diagnostic accuracy (lesion detectability) at browsing rates as low as 10 frames per second (fps). Recently, a multi-slice channelized Hotelling observer (msCHO) model was proposed to estimate the detection performance in 3D images. This implementation of the msCHO restricted the analysis to the luminance of a display pixel at the end of the frame time (end-of-frame luminance) while ignoring the luminance transition within the frame time (intra-frame luminance). Such an approach fails to differentiate between, for example, the commonly found case of two displays with different temporal profiles of luminance as long as their end-of-frame luminance levels are the same. In order to overcome this limitation of the msCHO, we propose a new upsampled msCHO (umsCHO) which acts on images obtained using both the intra-frame and the end-of-frame luminance information. The two models are compared on a set of synthesized 3D images for a range of browsing rates (16.67, 25 and 50 fps). Our results demonstrate that, depending on the details of the luminance transition profiles, neglecting the intra-frame luminance information may lead to over- or underestimation of lesion detectability. Therefore, we argue that using the umsCHO rather than msCHO model is more appropriate for estimating the detection performance in the stack-browsing mode.
Locally Adaptive Passive Error Concealment for Wavelet Coded Images

This letter presents a novel locally adaptive error concealment method for subband coded images. For each lost low-frequency coefficient, we estimate the optimal interpolation weights from its neighborhood. The calculation of the interpolation weights is optimized in the mean squared error sense, and it takes into account the errors that would arise by horizontally and vertically interpolating the available neighbors of the lost coefficient. Compared to methods of similar complexity, the proposed scheme estimates the lost coefficients more accurately: on average, the PSNR is increased by up to 4.5 dB. The reconstructed images also look better, and our method is fast and of low complexity.
Exploiting interframe redundancies in the lossless compression of 3D medical images

An evaluation is made of different approaches to removing interframe redundancies. The test images we used are the MRI and CT images available from the Visual Human Project. Firstly, we found that linear predictive techniques are not able to provide any compression improvement. Even an optimal linear predictor (least-squares optimization), which holds pixels from the current and the previous frames, does not outperform the very simple 2D lossless JPEG predictor number 7. However nonlinear techniques, such as context-modeling do yield better results. In a simple experiment, lossless JPEG was estimated to code intraframe prediction errors using an interframe context. The context for coding the current pixel is formed by the magnitude of the prediction error of the corresponding pixel in the previous frame. Note that this prediction error comes from the intraframe prediction step in the previous frame. Using this interframe context-modeling approach, a reduction of the bit rate of up to 1 bit per pixel is obtainable. It is obvious that the context serves as an edge-detector, albeit a very simple and straightforward one. A new technique is proposed based on the 2D lossless image compressor JPEG-LS. The existing context-modeling scheme is extended to also catch the interframe redundancies. The interframe context is very similar to the abovementioned one, however the sign of the prediction error in the previous frame is also used.
Weighted Sparse Graph Based Dimensionality Reduction for Hyperspectral Images

Dimensionality reduction (DR) is an important and helpful preprocessing step for hyperspectral image (HSI) classification. Recently, sparse graph embedding (SGE) has been widely used in the DR of HSIs. SGE explores the sparsity of the HSI data and can achieve good results. However, in most cases, locality is more important than sparsity when learning the features of the data. In this letter, we propose an extended SGE method: the weighted sparse graph based DR (WSGDR) method for HSIs. WSGDR explicitly encourages the sparse coding to be local and pays more attention to those training pixels that are more similar to the test pixel in representing the test pixel. Furthermore, WSGDR can offer data-adaptive neighborhoods, which results in the proposed method being more robust to noise. The proposed method was tested on two widely used HSI data sets, and the results suggest that WSGDR obtains sparser representation results. Furthermore, the experimental results also confirm the superiority of the proposed WSGDR method over the other state-of-the-art DR methods.
A markerless 3D tracking approach for augmented reality applications

Nowadays, augmented reality (AR) applications are still dominated by marker-based solutions to perform pose estimation. The main reason for this is that existing marker-less trackers are still not robust enough to serve the purpose of tracking the head of the user at all times. In this paper, we propose an improvement over existing camera trackers by using only depth information acquired by hand-held depth cameras. The main contribution of this paper is the way we combine a dense tracking approach with a model-based one. More specifically, besides pose estimation based on the alignment of consecutive point clouds, a second thread is running for the alignment of the acquired point cloud with a 3D model. To make the tracking both robust, accurate and fast, we propose a surface reconstruction step combined with a multi-resolution resampling of the 3D model. Our system was extensively tested on an online available dataset and the accuracy proved to be competitive with state-of-the-art visual SLAM methods.
MPEG-7 based dynamic metadata

In this paper, we describe a method for adding dynamic metadata to multimedia data, based on the emerging MPEG-7 standard. The method enables metadata to be dynamically added, removed, altered or processed in a generic way. Objects, interfaces and events (concepts from object technology) are defined using descriptors and descriptor schemes written in XML. By creating objects and interfaces, the content creator can add functionality to the metadata. This functionality can then be applied in an application dependant manner. Finally a preliminary implementation of the framework was created in order to validate the proposed methods. The current implementation uses the Apple QuickTime1 framework as a host framework. Using the QuickTime movie format and the QuickTime framework, a format for transporting and manipulating the metadata has been created.
Segmentation of brain blood vessels using projections in 3-D CT angiography images

Segmenting cerebral blood vessels is of great importance in diagnostic and clinical applications, especially in quantitative diagnostics and surgery on aneurysms and arteriovenous malformations (AVM). Segmentation of CT angiography images requires algorithms robust to high intensity noise, while being able to segment low-contrast vessels. Because of this, most of the existing methods require user intervention. In this work we propose an automatic algorithm for efficient segmentation of 3-D CT angiography images of cerebral blood vessels. Our method is robust to high intensity noise and is able to accurately segment blood vessels with high range of luminance values, as well as low-contrast vessels.
Adaptive noise removal from biomedical signals using warped polynomials

Presents the time-warped polynomial filter (TWPF), a new interval-adaptive filter for removing stationary noise from nonstationary biomedical signals. The filter fits warped polynomials to large segments of such signals. This can be interpreted as low-pass filtering with a time-varying cutoff frequency. In optimal operation, the filter's cut-off frequency equals the local signal bandwidth. However, the author also presents an iterative filter adaptation algorithm, which does not rely on the (complicated) computation of the local bandwidth. The TWPF has some important advantages over existing adaptive noise removal techniques: it reacts immediately to changes in the signal's properties, independently of the desired noise reduction; it does not require a reference signal and can be applied to nonperiodical signals. In case of quasiperiodical signals, applying the TWPF to the individual signal periods leads to an optimal noise reduction. However, the TWPF can also be applied to intervals of fixed size, at the expense of a slightly lower noise reduction. This is the way nonquasiperiodical signals are filtered. The author presents experimental results which demonstrate the usefulness of the interval-adaptive filter in several biomedical applications: noise removal from ECG, respiratory and blood pressure signals, and base-line restoration of electroencephalograms (EEGs).
Demo: Real-time indoors people tracking in scalable camera networks

In this demo we present a people tracker in indoor environments. The tracker executes in a network of smart cameras with overlapping views. Special attention is given to real-time processing by distribution of tasks between the cameras and the fusion server. Each camera performs tasks of processing the images and tracking of people in the image plane. Instead of camera images, only metadata (a bounding box per person) are sent from each camera to the fusion server. The metadata are used on the server side to estimate the position of each person in real-world coordinates. Although the tracker is designed to suit any indoor environment, in this demo the tracker's performance is presented in a meeting scenario, where occlusions of people by other people and/or furniture are significant and occur frequently. Multiple cameras insure views from multiple angles, which keeps tracking accurate even in cases of severe occlusions in some of the views.
Constructing the topological solution of jigsaw puzzles

In this paper we present a novel approach to the jigsaw puzzle solving problem. The main components are a shape based local contour matching followed by a global solving procedure that constructs the topological solution of the jigsaw puzzle. The shape based local contour matching is discussed briefly, but the main focus of the paper is the construction of the topological solution. The solving procedure starts with the classification of the puzzle pieces. Next, the edge topology is constructed and finally the internal topology is constructed. We tested the developed algorithms on five different jigsaw puzzles. Ultimately, we were able to solve a jigsaw puzzle consisting of 300 puzzle pieces, the largest one solved automatically to date.
Variational multi-image stereo matching

In two-view stereo matching, the disparity of occluded pixels cannot accurately be estimated directly: it needs to be inferred through, e.g., regularisation. When capturing scenes using a plenoptic camera or a camera dolly on a track, more than two input images are available, and - contrary to the two-view case - pixels in the central view will only very rarely be occluded in all of the other views. By explicitly handling occlusions, we can limit the depth estimation of pixel p to only use those cameras that actually observe p. We do this by extending variational stereo matching to multiple views, and by explicitly handling occlusion on a view-by-view basis. Resulting depth maps are illustrated to be sharper and less noisy than typical recent techniques working on light fields.
Optimizing image quality using test signals: Trading off blur, noise and contrast

Objective image quality assessment (QA) is crucial in order to improve imaging systems and image processing techniques. In medical imaging, model observers that estimate signal detectability, have become widespread and promising as a means to avoid costly human observer experiments. However, signal detectability alone does not give the complete picture: one may also be interested in optimizing several independent quality factors (e.g. contrast, spatial resolution, noise). In recent work, we have proposed the channelized joint observer (CJO), to jointly detect and estimate random parametric signals in images, a so-called signal-known-statistically (SKS) detection task. In this paper, we show how the estimation capabilities of the CJO can be exploited to estimate several image quality factors in degraded images, through signal insertion. By fixing the signal detectability, we illustrate how to benefit from the trade-offs that exist between the different quality factors. Our method is in the first place intended to aid medical image reconstruction techniques and medical display design, although the technique can also be useful in a much wider context.
EM-based estimation of spatially variant correlated image noise

In image denoising applications, noise is often correlated and the noise energy and correlation structure may even vary with the position in the image. Existing noise reduction and estimation methods are usually designed for stationary white Gaussian noise and generally work less efficient in this case because of the noise model mismatch. In this paper, we propose an EM algorithm for the estimation of spatially variant (nonstationary) correlated image noise in the wavelet domain. In particular, we study additive white Gaussian noise filtered by a space-variant linear filter. This general noise model is applicable to a wide variety of practical situations, including noise in Computed Tomography (CT). Results demonstrate the effectiveness of the proposed solution and its robustness to signal structures.
Performance of Four Subjective Video Quality Assessment Protocols and Impact of Different Rating Preprocessing and Analysis Methods

Standardization bodies recommend various protocols to conduct subjective quality assessment (QA) of imaging systems. While many studies have compared these QA protocols, few have assessed the impact of different approaches for preprocessing and analyzing quality ratings. Furthermore, the effect of large versus small quality differences on the discrimination performance of protocols has not been extensively studied in video QA. This study examines these issues for four QA protocols. H.264 compressed medical videos and denoised natural scene videos were evaluated by expert and naive subjects. Scores were collected with four QA protocols—forced choice (FC), two ratio-scaled paired comparison methods: preference (Pref) and dissimilarity (Dissim), and single stimulus (SS)—and analyzed using combinations of different rating preprocessing approaches, generating a total of 14 methods. Performance metrics—probability and effect size—quantified the ability of the methods to discriminate between quality levels. The Pref and Dissim methods analyzed with classical multidimensional scaling and the SS method with Z-score transformation consistently outperformed the other methods. The type of preprocessing introduced large differences in the performance of individual protocols. Grouping stimuli pairs by small and large quality differences introduced significant differences in the performance rankings of the methods, with Pref and Dissim being most sensitive to small quality differences. For the future, we suggest further validation of the FC method, due to its simplicity and ease of use, and continued investigation into more robust raw scores transformation and statistical analysis methods for both SS and FC ratings.
A primal-dual algorithm for joint demosaicking and deconvolution

In this paper, we present a first-order primal-dual algorithm for tackling the joint demosaicking and deconvolution problem. The proposed algorithm exploits the sparsity of both discrete gradient (TV) and shearlet coefficients as prior knowledge. In order to deal with this sparsity across the color channels, we first decorrelate the signals in color space before sparsifying them spatially, resulting in a separable transform. We demonstrate that this approach yields better results than employing group sparsity strategies. We propose to update the decorrelation operator during the image reconstruction, this approach will result in a significant improvement in PSNR. By relaxing the sparsity of the chrominance signals, we obtain both better objective and subjective image quality compared to other state-of-the-art demosaicking and deconvolution algorithms. Also, color artifacts due to demosaicking are suppressed very well.
Classification of cloudy hyperspectral image and LiDAR data based on feature fusion and decision fusion

Hyperspectral and LiDAR data, can provide plentiful information about the objects on the Earths surface. However there are some shortages for each of them, where hyperspectral sensor is easily influenced by cloud and difficult to distinguish different objects contained same materials, LiDAR cannot discriminate different objects which are similar in altitude. Fusion of these multi-source data for reliable classification attracts increasing interests but remains challenging. In this paper, we propose a new framework to fuse multi-source data for classification. The proposed method contains three main works: 1) cloud shadows extraction; 2) feature fusion of spectral and spatial information extracted from hyperspectral image, elevation information extracted from LiDAR data; 3) decision fusion of cloud and non-cloud regions. Experimental results on real HSI and LiDAR data demonstrate effectiveness of the proposed method both visually and quantitatively.