nils-herrmann/clean_scirepeval huggingface dataset:
Integrated approach for estimation and restoration of photon-limited images based on steerable pyramids

In disciplines like fluorescence microscopy and astronomical imaging, the imaging process is based on detection of photons. Fluctuations in photon counting processes are described by Poisson statistics. In this paper, a new combined method based on steerable pyramids is proposed for the estimation of the degradation parameters (like noise and blur) and the restoration of photon-limited images. It consists of the following steps: in the steerable pyramid domain, a noise suppression step is performed, followed by a blur estimation. As a last step, the Richardson-Lucy deconvolution algorithm is applied. These steps are iterated. The only free parameter in the algorithm is the number of iterations, but an empirical stopping rule is suggested in terms of the blur estimation. Otherwise, this method is fully automatic and provides very nice restoration results.
Centerline calculation for extracting abdominal aorta in 3-D MRI images

The examination of abdominal aorta is an effective way to diagnose many cardiovascular diseases. Aortic stiffness measured by pulse wave velocity (PWV) calculation is a good estimate of overall cardiovascular health. Calculation of pulse wave velocity requires the length of abdominal aorta as an input parameter, while the structure of abdominal aorta can be used for diagnostic purposes. For the sake of non-invasive diagnostics, non-contrasted MRI images of aorta were used. Due to the “black-blood” imaging, a lot of artifacts are present and a robust centerline extraction method is needed. In this research we develop a novel graph-based method for extracting centerlines of abdominal aorta for length calculation. Our method is robust to artifacts and noise and applicable to any imaging modality.
Rotation invariant similarity measure for non-local self-similarity based image denoising

Non-local self-similarity based image denoising depends strictly on similarity measure. The denoising performance is determined based on the ability to reliably find sufficient number of similar patches. In this paper, we propose a rotation invariant similarity measure to fully exploit the image non-local self-similarity. Instead of using image patches, we employ local frequency descriptors, that are rotation invariant and robust to noise, to measure the similarity. Thus, both translational and rotational similarity can be handled even at high noise level. The comparative experimental results show that the proposed method is effective as a rotation invariant similarity measure, and it can consistently improve the performance of non-local means algorithm to achieve better denoising results.
Detecting variable source areas from temporal radar imagery using advanced image enhancement techniques

Recently, N. E. C. Verhoest et al. (1998) showed that it is possible to map variable source areas in a catchment using a principal component analysis. This technique, based on a temporal series of images, revealed the spatial soil moisture patterns from the vegetation and topographic effects introduced in a synthetic aperture radar (SAR) image. However, the obtained image is still corrupted with noise, which is partially related to the speckle observed within a SAR image. In order to get a noiseless image, which is more appropriate for hydrological modelling schemes, the authors apply a recently developed wavelet-based image denoising technique, A. Pizurica et al. (1999). The main advantage of this filtering technique is that it preserves the spatial patterns and observed edges, while it increases the signal to noise ratio significantly. The suitability of this denoising algorithm is investigated by comparing the hydrologic information included in these visually well-appearing images with the results obtained for their non-filtered counterparts.
A noise robust method for change detection

Motion and other changes in video sequences can be detected by analyzing the differences between grey levels of successive frames. The simplest method to do this, is to subtract the grey values of corresponding pixels of two consecutive frames. This is a pixel based technique: if there is a difference, the pixel in question is considered to be on or near to a moving object. This assumption is clearly not always correct because changes in grey scale may also be caused by noise. In this paper, we present a technique for change detection that has low noise-sensitivity and that has a low complexity. The technique combines recursive temporal filtering with local neighborhood conditioning.
Locally Adaptive Intrasubband Interpolation of Lost Lowfrequency Coefficients Inwavelet Coded Images

This paper presents a novel passive error concealment method for wavelet coded images. The proposed method is a locally adaptive directional interpolation approach, where the interpolation weights are estimated based on the available local context. For each lost low frequency coefficient, we estimate the optimal interpolation weights based on the errors that would arise by horizontally and vertically interpolating the available neighbors of the lost coefficient. Compared to older methods of similar complexity, the proposed scheme estimates the lost coefficients much better: on average, the PSNR is increased with up to 0.6 dB. The results also indicate improvements over the best available state-of-the-art techniques. The reconstructed images also look better. As our method is fast and of low complexity, it is widely usable.
Consistent ICP for the registration of sparse and inhomogeneous point clouds

In this paper, we derive a novel iterative closest point (ICP) technique that performs point cloud alignment in a robust and consistent way. Traditional ICP techniques minimize the point-to-point distances, which are successful when point clouds contain no noise or clutter and moreover are dense and more or less uniformly sampled. In the other case, it is better to employ point-to-plane or other metrics to locally approximate the surface of the objects. However, the point-to-plane metric does not yield a symmetric solution, i.e. the estimated transformation of point cloud p to point cloud q is not necessarily equal to the inverse transformation of point cloud q to point cloud p. In order to improve ICP, we will enforce such symmetry constraints as prior knowledge and make it also robust to noise and clutter. Experimental results show that our method is indeed much more consistent and accurate in presence of noise and clutter compared to existing ICP algorithms.
Exploring contour and texture features for context-aware patch-based inpainting

In this paper, we explore the use of contour and texture features for context-aware patch-based image inpainting. Both of these features are obtained by analysing the image filtered with the bank of filters at multiple orientations and scales, specifically Gabor filters. We use contour features to define a novel patch priority, which represents the main contribution of this paper. The priority is used to determine the filling order of the missing region, which is crucial for the success of the algorithm. Our goal is to make better differentiation between patches with structured, textured and smooth content than related definitions. We employ this novel priority within our recently proposed context-aware inpainting method, which uses contextual descriptors to find contextually similar image regions to which the search for well matching replacement patches is constrained. Here we use texture features, together with color features, as contextual descriptors of image regions. The benefit of the context-aware approach is twofold: the chance of choosing wrong matches is reduced and the search for candidate patches is accelerated. Experimental results demonstrate the benefit of the proposed method compared to state-of-the-art.
Automatic registration of synthetic aperture radar (SAR) images

The huge amount of incoming synthetic aperture radar (SAR) data nowadays demands the need for automatic image registration. Due the presence of speckle noise and the huge size of SAR images, registering SAR images is more difficult than traditional image registration methods. In this paper we present an automatic hierarchical registration method, based on polar block matching, that can handle the registration of SAR images efficiently. Results show us that our proposed registration method outperforms the manual registration and that our method is very robust against speckle noise. An additional advantage of our registration method is that there is no need for a despeckling preprocessing step
Edge Linking Based Method to Detect and Separate Individual C. Elegans Worms in Culture

Nowadays an increasing research interest in the field of biotechnology has been drawn to achieve reliable information from model organisms. C. Elegans nematode worm is one of the major animals. Machine vision analysis of this animal needs to solve many important problems, e.g. detection of each individual in population images, movement patterns of isolated and overlapped worms and so on. In this paper, we describe our recently proposed method with an analysis to show the impact of two major parameters on the method efficiency. Based on our analysis on 255 isolated and overlapped worms, we find that our method prepares the best correct detection (TAR=83%) for thetasTh and NTh between 15 and 20 pixels.
Gradient - based optical flow for sub-pixel registration of speckle image sequences using a spatial/temporal postprocessing technique

Digital image processing techniques are becoming a popular way of determining strains and full-field displacements in experimental mechanics due to advancements in image processing techniques and also because of the non intrusive way in which these measurements are done compared to traditional sensor based methods. This paper presents a polar component filtering technique for the image displacement fields in which two filters are used: a Kalman filter for temporal smoothing of the motion vector angles and a subsequent adaptive spatial filter for filtering both previously processed angles and amplitudes of the vectors.
LiDAR information extraction by attribute filters with partial reconstruction

Recent advances in airborne light detection and ranging (LiDAR) technology allow us to rapid measure the topographical information over large areas. LiDAR remote sensed data has been widely used in many applications, e.g. forest management, urban planning, disaster predictions, etc. However, extracting useful information from LiDAR data remains challenging, especially in the urban remote sensing, where many objects have the same elevation and are connected, such as road and parking lots, trees and buildings. In this work, we present a new method to extract geometric and textural information from LiDAR data by using attribute filters with partial reconstruction. The proposed method can separate the connected objects and better model the geometric and textural information than traditional connected filters (e.g. attribute filters). Experimental results on LiDAR data from the 2013  Data Fusion Contest demonstrate effectiveness of the proposed method. Compared to the methods using original LiDAR data or attribute profiles computed by traditional attribute filters, with the proposed method, overall classification accuracies were improved by 35% and 12%, respectively.
Non-Local Image Interpolation

In this paper we present a novel method for interpolating images and we introduce the concept of non-local interpolation. Unlike other conventional interpolation methods, the estimation of the unknown pixel values is not only based on its local surrounding neighbourhood, but on the whole image (non-locally). In particularly, we exploit the repetitive character of the image. A great advantage of our proposed approach is that we have more information at our disposal, which leads to better estimates of the unknown pixel values. Results show the effectiveness of non-local interpolation and its superiority at very large magnifications to other interpolation methods.
Brain volume segmentation in newborn infants using multi-modal MRI with a low inter-slice resolution

Brain volume segmentation from neonatal magnetic resonance images (MRI) offers the possibility of exploring the developmental changes, measuring the brain growth, detecting early disorders and three-dimensional (3D) volume reconstruction. However, such segmentation is challenging mainly due to the fast growth process, complex anatomy of the developing brain and often poor MRI quality. Existing techniques are mainly developed for adult brain and are not applicable to neonates or require additional corrections. In this paper we present an algorithm for brain volume segmentation in neonates using T1-weighted (T1-w) and T2-weighted (T2-w) MRI with a low inter-slice resolution. The method incorporates both intensity and edge information and consists of three main steps: image pre-processing, brain segmentation and 3D brain reconstruction. Our algorithm is tested on real neonatal brain MRI with a gestational age between 39–41 weeks and achieves performance comparable to manual segmentation. Also, experimental segmentation results show that our method is effective and more accurate than segmentation methods originally developed for adults.
Fusion of Hyperspectral and LiDAR Data for Classification of Cloud-Shadow Mixed Remote Sensed Scene

Recent advances in sensor design allow us to gather more useful information about the Earth's surface. Examples are hyperspectral (HS) and Light Detection And Ranging (LiDAR) sensors. These, however, have limitations. HS data cannot distinguish different objects made from similar materials and highly suffers from cloud-shadow regions, whereas LiDAR cannot separate distinct objects that are at the same altitude. For an increased classification performance, fusion of HS and LiDAR data recently attracted interest but remains challenging. In particular, these methods suffer from a poor performance in cloud-shadow regions because of the lack of correspondence with shadow-free regions and insufficient training data. In this paper, we propose a new framework to fuse HS and LiDAR data for the classification of remote sensing scenes mixed with cloud-shadow. We process the cloud-shadow and shadow-free regions separately, our main contribution is the development of a novel method to generate reliable training samples in the cloud-shadow regions. Classification is performed separately in the shadow-free (classifier is trained by the available training samples) and cloud-shadow regions (classifier is trained by our generated training samples) by integrating spectral (i.e., original HS image), spatial (morphological features computed on HS image) and elevation (morphological features computed on LiDAR) features. The final classification map is obtained by fusing the results of the shadow-free and cloud-shadow regions. Experimental results on a real HS and LiDAR dataset demonstrate the effectiveness of the proposed method, as the proposed framework improves the overall classification accuracy with 4% for whole scene and 10% for shadow-free regions over the other methods.
Communicationless navigation through robust visual odometry

GPS navigation is often found undependable in urban situations where tall structures occlude large parts of the sky. To keep accurate position in these situations, we need an alternative method. We propose a novel visual odometry method that is shown to provide reliable relative motion estimation in typical urban road driving using a single camera. While the short-term accuracy is good, relative motion estimation by itself is susceptible to drift and therefore insufficient to provide good long-term absolute position estimates. To overcome this drift, we propose to warp the visual odometry output to a stored map. This warping must be able to cope with temporary discrepancies between visual odometry and map data. The proposed mapping does not make a hard decision about road position, but instead entertains all plausible hypotheses about the followed trajectory and their associated warping costs. Evaluation on real test sequences proves that the method succesfully eliminates drift, and on average stays within 7 metres of simultaneously recorded GPS data. This proves that the combined visual odometry and mapping are sufficient to provide positioning with comparable accuracy to GPS in those situations when GPS is unavailable.
Removal of Correlated Noise by Modeling the Signal of Interest in the Wavelet Domain

Images, captured with digital imaging devices, often contain noise. In literature, many algorithms exist for the removal of white uncorrelated noise, but they usually fail when applied to images with correlated noise. In this paper, we design a new denoising method for the removal of correlated noise, by modeling the significance of the noise-free wavelet coefficients in a local window using a new significance measure that defines the ldquosignal of interestrdquo and that is applicable to correlated noise. We combine the intrascale model with a hidden Markov tree model to capture the interscale dependencies between the wavelet coefficients. We propose a denoising method based on the combined model and a less redundant wavelet transform. We present results that show that the new method performs as well as the state-of-the-art wavelet-based methods, while having a lower computational complexity.
Sparse optical flow regularization for real-time visual tracking

Optical flow can greatly improve the robustness of visual tracking algorithms. While dense optical flow algorithms have various applications, they can not be used for real-time solutions without resorting to GPU calculations. Furthermore, most optical flow algorithms fail in challenging lighting environments due to the violation of the brightness constraint. We propose a simple but effective iterative regularisation scheme for real-time, sparse optical flow algorithms, that is shown to be robust to sudden illumination changes and can handle large displacements. The algorithm proves to outperform well known techniques in real life video sequences, while being much faster to calculate. Our solution increases the robustness of a real-time particle filter based tracking application, consuming only a fraction of the available CPU power. Furthermore, a new and realistic optical flow dataset with annotated ground truth is created and made freely available for research purposes.
Noise reduction by fuzzy image filtering

A new fuzzy filter is presented for the noise reduction of images corrupted with additive noise. The filter consists of two stages. The first stage computes a fuzzy derivative for eight different directions. The second stage uses these fuzzy derivatives to perform fuzzy smoothing by weighting the contributions of neighboring pixel values. Both stages are based on fuzzy rules which make use of membership functions. The filter can be applied iteratively to effectively reduce heavy noise. In particular, the shape of the membership functions is adapted according to the remaining noise level after each iteration, making use of the distribution of the homogeneity in the image. A statistical model for the noise distribution can be incorporated to relate the homogeneity to the adaptation scheme of the membership functions. Experimental results are obtained to show the feasibility of the proposed approach. These results are also compared to other filters by numerical measures and visual inspection.
Wavelet based denoising techniques for ultrasound images

Clinical research has shown a clear correlation between white matter disorders of the neonatal brain and neuro-motoric handicap at a later age. The visual interpretation of ultrasound images is a proven method to detect white matter damage at in early stage. A problem, common to all medical ultrasound images, is the presence of speckle noise, which not only complicates the visual interpretation of images, but also the quantitative measurements. In this article we compare two wavelet based filtering methods which are applied in order to remove speckle noise and preserve details as well. The first one is a new wavelet-based method for image denoising that applies the Bayesian framework, using prior knowledge about the spatial clustering of the wavelet coefficients. Local spatial interactions of the wavelet coefficients are modeled by adopting a Markov random field model. The second one is a new filtering method, based on a recursive filtering of detail images obtained from a wavelet decomposition of the image by using spatial filtering which is based on threshold decomposition and simple spatial rules.
Pixel profiling for extraction of arteriovenous malformation in 3-D CTA images

Cerebral arteriovenous malformation (AVM) presents a great health threat due to its high probability of rupture which can cause severe brain damage or even death. For planing the embolization procedure of an AVM, the knowledge of the accurate location and size of the malformation is of utmost importance. The main purposes of automatic AVM segmentation are: 1) objective and reproducible segmentation; 2) reduction in processing time (saving resources by requiring less manual work). Furthermore, automatic segmentation with accurate AVM (or aneurysm) characterization were deemed helpful in therapeutic decision making concerning treatment modality (surgical or endovascular). Operator-independent accurate sizing of AVM (aneurysm) would allow strict follow-up until the threshold is reached and the patient referred to treatment. We propose in this paper a novel AVM detection method and a blood vessel tree analysis approach using ordered thinning-based skeletonization. The main contributions are: (1) a new method of profile volume calculation to replace the distance labels in ordered skeletonization; (2) an automatic method for AVM detection and extraction, with accurate positioning and malformation size estimation. The main idea in our work is use the structural (anatomical) vessel differences and the inhomogeneities in distribution of pixel gray values to locate and extract the AVM. The algorithm takes a segmentation result as an input to perform AVM delineation. The algorithm determines the AVM region automatically, without any user interaction and independently of the segmentation algorithm used. The proposed approach is validated on brain blood vessel CTA images before and after embolization. The results obtained using the Dice coefficient comparisons, the volume percent error and the AVM center position show high accuracy of our method and indicate potentials for use in surgical planning.
Semisupervised Local Discriminant Analysis for Feature Extraction in Hyperspectral Images

We propose a novel semisupervised local discriminant analysis method for feature extraction in hyperspectral remote sensing imagery, with improved performance in both ill-posed and poor-posed conditions. The proposed method combines unsupervised methods (local linear feature extraction methods and supervised method (linear discriminant analysis) in a novel framework without any free parameters. The underlying idea is to design an optimal projection matrix, which preserves the local neighborhood information inferred from unlabeled samples, while simultaneously maximizing the class discrimination of the data inferred from the labeled samples. Experimental results on four real hyperspectral images demonstrate that the proposed method compares favorably with conventional feature extraction methods.