Sharpness Metric Research Articles

Sorted Golden-step phase encoding: an improved Golden-step imaging technique for cardiac and respiratory self-gated cine cardiovascular magnetic resonance imaging

BackgroundNumerous self-gated cardiac imaging techniques have been reported in the literature. Most can track either cardiac or respiratory motion, and many incur some overhead to imaging data acquisition. We previously described a Cartesian cine imaging technique, pseudo-projection motion tracking with golden-step phase encoding, capable of tracking both cardiac and respiratory motion at no cost to imaging data acquisition. In this work, we describe improvements to the technique by dramatically reducing its vulnerability to eddy current and flow artifacts and demonstrating its effectiveness in expanded cardiovascular applications.MethodsAs with our previous golden-step technique, the Cartesian phase encodes over time were arranged based on the integer golden step, and readouts near ky = 0 (pseudo-projections) were used to derive motion. In this work, however, the readouts were divided into equal and consecutive temporal segments, within which the readouts were sorted according to ky. The sorting reduces the phase encode jump between consecutive readouts while maintaining the pseudo-randomness of ky to sample both cardiac and respiratory motion without comprising the ability to retrospectively set the temporal resolution of the original technique. On human volunteers, free-breathing, electrocardiographic (ECG)-free cine scans were acquired for all slices of the short axis stack and the 4-chamber view of the long axis. Retrospectively, cardiac motion and respiratory motion were automatically extracted from the pseudo-projections to guide cine reconstruction. The resultant image quality in terms of sharpness and cardiac functional metrics was compared against breath-hold ECG-gated reference cines.ResultsWith sorting, motion tracking of both cardiac and respiratory motion was effective for all slices orientations imaged, and artifact occurrence due to eddy current and flow was efficiently eliminated. The image sharpness derived from the self-gated cines was found to be comparable to the reference cines (mean difference less than 0.05 mm− 1 for short-axis images and 0.075 mm− 1 for long-axis images), and the functional metrics (mean difference < 4 ml) were found not to be statistically different from those from the reference.ConclusionsThis technique dramatically reduced the eddy current and flow artifacts while preserving the ability of cost-free motion tracking and the flexibility of choosing arbitrary navigator zone width, number of cardiac phases, and duration of scanning. With the restriction of the artifacts removed, the Cartesian golden-step cine imaging can now be applied to cardiac imaging slices of more diverse orientation and anatomy at greater reliability.

Evaluating post-processing approaches for monthly and seasonal streamflow forecasts

Abstract. Streamflow forecasting is prone to substantial uncertainty due to errors in meteorological forecasts, hydrological model structure, and parameterization, as well as in the observed rainfall and streamflow data used to calibrate the models. Statistical streamflow post-processing is an important technique available to improve the probabilistic properties of the forecasts. This study evaluates post-processing approaches based on three transformations – logarithmic (Log), log-sinh (Log-Sinh), and Box–Cox with λ=0.2 (BC0.2) – and identifies the best-performing scheme for post-processing monthly and seasonal (3-months-ahead) streamflow forecasts, such as those produced by the Australian Bureau of Meteorology. Using the Bureau's operational dynamic streamflow forecasting system, we carry out comprehensive analysis of the three post-processing schemes across 300 Australian catchments with a wide range of hydro-climatic conditions. Forecast verification is assessed using reliability and sharpness metrics, as well as the Continuous Ranked Probability Skill Score (CRPSS). Results show that the uncorrected forecasts (i.e. without post-processing) are unreliable at half of the catchments. Post-processing of forecasts substantially improves reliability, with more than 90 % of forecasts classified as reliable. In terms of sharpness, the BC0.2 scheme substantially outperforms the Log and Log-Sinh schemes. Overall, the BC0.2 scheme achieves reliable and sharper-than-climatology forecasts at a larger number of catchments than the Log and Log-Sinh schemes. The improvements in forecast reliability and sharpness achieved using the BC0.2 post-processing scheme will help water managers and users of the forecasting service make better-informed decisions in planning and management of water resources. Highlights. Uncorrected and post-processed streamflow forecasts (using three transformations, namely Log, Log-Sinh, and BC0.2) are evaluated over 300 diverse Australian catchments. Post-processing enhances streamflow forecast reliability, increasing the percentage of catchments with reliable predictions from 50 % to over 90 %. The BC0.2 transformation achieves substantially better forecast sharpness than the Log-Sinh and Log transformations, particularly in dry catchments.

A Local Metric for Defocus Blur Detection Based on CNN Feature Learning.

Defocus blur detection is an important and challenging task in computer vision and digital imaging fields. Previous work on defocus blur detection has put a lot of effort into designing local sharpness metric maps. This paper presents a simple yet effective method to automatically obtain the local metric map for defocus blur detection, which based on the feature learning of multiple convolutional neural networks (ConvNets). The ConvNets automatically learn the most locally relevant features at the super-pixel level of the image in a supervised manner. By extracting convolution kernels from the trained neural network structures and processing it with principal component analysis, we can automatically obtain the local sharpness metric by reshaping the principal component vector. Meanwhile, an effective iterative updating mechanism is proposed to refine the defocus blur detection result from coarse to fine by exploiting the intrinsic peculiarity of the hyperbolic tangent function. The experimental results demonstrate that our proposed method consistently performed better than the previous state-of-the-art methods.

A Fuzzy Sharpness Metric for Magnetic Resonance Images

A fuzzy-based sharpness metric for the objective measurement of sharpness of Magnetic Resonance (MR) images is proposed in this paper. In the proposed metric, Quadratic Index of fuzziness (QIF) is used to quantitatively express image sharpness. The proposed metric is found to be superior to Maximum Local Variation (MLV) metric, Perceptual Sharpness Index (PSI), Second order Derivative based Measure of Enhancement (SDME), Blanchet’s Sharpness Index (BSI) and Roffet’s Blur Metric (RBM) in terms of correlation with subjective quality ratings and computational time.

A hybrid defocused region segmentation approach using image matting

In this paper, a hybrid defocused region segmentation using image matting is proposed. The technique incorporates three sharpness metrics which are magnitude spectrum slope, local total variation and local binary patterns to identify the in-focus pixels in the image. Trimap is generated automatically using sharpness maps to obtain the prior information and matting Laplacian is applied to propagate the trimap to the entire image based on color similarities. Simulation results compared using visual and quantitative metrics show the strength of the proposed technique.

Comparing the similarity of sharpness predictions based on specific loudness from ANSI S3.4-2007 with those according to DIN 45692:2009 for several real-world sound classes

A previous paper [Swift and Gee, Proc. Mtgs. Acoust., p. 030001, 2017] showed methods for transforming the standardized form of sharpness to accept inputs from the ANSI S3.4-2007 loudness standard. A further recent publication [Swift and Gee, JASA-EL, 2017] showed additional validation results comparing the results of the simplest version of the transformed metric for a set of 40 real-world sounds. In this paper, three versions of the ANSI-based sharpness metric are analyzed, in order to determine which of the three implementations gives the most similar results to the original DIN 45692:2009 standard. Finally, classes of sounds are identified for which predictions between the three ANSI-based sharpness metrics and DIN 45692:2009 may differ to a greater or lesser extent.

Adaptive particle image velocimetry based on sharpness metrics

BackgroundOptical distortions can significantly deteriorate the measurement accuracy in particle image velocimetry systems. Such distortions can occur at fluctuating phase boundaries during flow measurement and result from the accompanied refractive index changes. The usage of a wavefront sensor can be hindered by disturbing light reflexes or scattering.MethodsA combination of sharpness metric image evaluation and iterative optimization is demonstrated. The sharpness metric is used as an indicator for wavefront aberrations in order to correct low-order Zernike modes that influence the image quality of particle image velocimetry.ResultsIn this work we outline a sharpness metric based aberration correction with a deformable mirror, applied for the first time to particle image velocimetry. The proposed method allows for the reduction of systematic measurement uncertainties in particle image velocimetry. ConclusionOur approach offers a new way to reduce static or slowly changing wavefront distortions in a fluid flow measurement setup in which a wavefront sensor is not applicable.

A No-Reference Image Sharpness Metric Based on Large-Scale Structure

Most of no-reference image sharpness metrics suffer interference of tiny textures and the contrast around edges. Based on large-scale structure, this paper proposes a novel metric to avoid this problem. Firstly prominent edges are extracted by a weighted least-squares filter. Then, we calculate the statistics proportion of edges with different width and the mean value of the maximum gradients from the 51th to 250th as the features of the image. Finally, the support vector regression is used to get the relationship model between the features and the subjective assessment result. The metric can reduce the influence of textures, contrast and the content of images effectively, accordingly enhancing the assessment accuracy.

No-reference image quality assessment method based on visual attention mechanism and sharpness metric approach

A no-reference image quality assessment method based on bottom-up visual attention mechanism and top-down image sharpness metric is proposed for the blur image quality evaluation.Firstly, the color, intensity and orientation feature maps are calculated based on the human visual attention mechanism, and then the saliency region of the image is obtained by the winner-take-all competition. Secondly, the image of saliency region and the background region are evaluated using no-reference image sharpness evaluation method, and the final image quality index can be obtained by the combination of the above two results.The radial blur images produced in the forward motion imaging and the Gauss fuzzy images in the Live database evaluation are evaluated using the proposed method respectively.Experiment results can prove that the correlation coefficient between the results of our method and the subjective one is larger than 0.98 for the radial blur images.The method can be used for evaluating the blur image subjectively.

EVALUATION OF DIYARBAKIR GAZI STREET SOUND ENVIRONMENT PERCEPTION BY SOUNDSCAPE APPROACH

In this study, it was aimed to measure the perception of sound environment of users in urban spaces with sound quality metrics (loudness, sharpness, roughness, and fluctuation strength). For this purpose, a questionnaire was applied to participants in Gazi Street, Surici district of Diyarbakir, which was selected as a field study. In the questionnaire study, participants were asked about the demographic / personal characteristics, questions about the purpose and duration of use of the space, and 35 adjective pairs with 5 bipolar scales in order to determine the perception of the sound environment. Binaural sound recordings were also performed simultaneously when the survey was conducted. The quantitative data of the loudness, sharpness, roughness, and fluctuation strength metrics of these sound records are calculated. By analyzing the results obtained from the questionnaire and the quantitative data of the sound recordings, correlations between the sound environment perception of the users and the sound quality metrics were determined. Apart from the quantitative data of sound recordings, the effect of the parameters (demographic / personal / social) which can affect the perception of the sound environment in urban spaces has been tried to be determined. In addition, the relationship between quantitative data of sound recordings and meteorological data has also been analyzed.

Super-Resolution Axial Localization of Ultrasound Scatter Using Multi-Focal Imaging

This paper aims to develop a method for achieving micrometre axial scatterer localization for medical ultrasound, surpassing the inherent, pulse length dependence limiting ultrasound imaging. The method, directly translated from cellular microscopy, is based on multi-focal imaging and the simple, aberration-dependent, image sharpness metric of a single point scatterer. The localization of a point scatterer relies on the generation of multiple overlapping sharpness curves, created by deploying three foci during receive processing, and by assessing the sharpness values after each acquisition as a function of depth. Each derived curve peaks around the receive focus and the unique position of the scatterer is identified by combining the data from all curves using a maximum likelihood algorithm with a calibration standard. Simulated and experimental ultrasound point scatter data show that the sharpness method can provide scatterer axial localization with an average accuracy down to 10.21 m ( 21) and with up to 11.4 times increased precision compared to conventional localization. The improvements depend on the rate of change of sharpness using each focus, and the signal to noise ratio in each image. Super-resolution axial imaging from optical microscopy has been successfully translated into ultrasound imaging by using raw ultrasound data and standard beamforming. The normalized sharpness method has the potential to be used in scatterer localization applications and contribute in current super-resolution ultrasound imaging techniques.

Uncertainty characterization of particle location from refocused plenoptic images

Plenoptic imaging is a 3D imaging technique that has been applied for quantification of 3D particle locations and sizes. This work experimentally evaluates the accuracy and precision of such measurements by investigating a static particle field translated to known displacements. Measured 3D displacement values are determined from sharpness metrics applied to volumetric representations of the particle field created using refocused plenoptic images, corrected using a recently developed calibration technique. Comparison of measured and known displacements for many thousands of particles allows for evaluation of measurement uncertainty. Mean displacement error, as a measure of accuracy, is shown to agree with predicted spatial resolution over the entire measurement domain, indicating robustness of the calibration methods. On the other hand, variation in the error, as a measure of precision, fluctuates as a function of particle depth in the optical direction. Error shows the smallest variation within the predicted depth of field of the plenoptic camera, with a gradual increase outside this range. The quantitative uncertainty values provided here can guide future measurement optimization and will serve as useful metrics for design of improved processing algorithms.

Localization of cardiac volume and patient features in inverse geometry x-ray fluoroscopy.

The scanning-beam digital x-ray (SBDX) system is an inverse geometry x-ray fluoroscopy technology that performs real-time tomosynthesis at planes perpendicular to the source-detector axis. The live display is a composite image which portrays sharp features (e.g. coronary arteries) extracted from a 16 cm thick reconstruction volume. We present a method for automatically determining the position of the cardiac volume prior to acquisition of a coronary angiogram. In the algorithm, a single non-contrast frame is reconstructed over a 44 cm thickness using shift-and-add digital tomosynthesis. Gradient filtering is applied to each plane to emphasize features such as the cardiomediastinal contour, diaphragm, and lung texture, and then sharpness vs. plane position curves are generated. Three sharpness metrics were investigated: average gradient in the bright field, maximum gradient, and the number of normalized gradients exceeding 0.5. A model correlating the peak sharpness in a non-contrast frame and the midplane of the coronary arteries in a contrast-enhanced frame was established using 37 SBDX angiographic loops (64-136 kg human subjects, 0-30° cranial-caudal). The average gradient in the bright field (primarily lung) and the number of normalized gradients >0.5 each yielded peaks correlated to the coronary midplane. The rms deviation between the predicted and true midplane was 1.57 cm. For a 16 cm reconstruction volume and the 5.5-11.5 cm thick cardiac volumes in this study, midplane estimation errors of 2.25-5.25 cm were tolerable. Tomosynthesis-based localization of cardiac volume is feasible. This technique could be applied prior to coronary angiography, or to assist in isocentering the patient for rotational angiography.

A reduced reference video quality assessment of H.264 and mpeg2 codec based on sharpness metric

AbstractIn this paper, we propose a new perceptually significant video quality metric for the H.264/Motion Picture Expert Group (MPEG)‐4 Advanced Video Coding (AVC) and MPEG2 standard. Our method operates in the spatial domain by using the Sobel filter. The proposed approach does not require a high computational complexity and can be suitable for real‐time evaluation. We evaluate the performance of the proposed method by using three Common Intermediate Format sequences at different compression rates. The comparison of the obtained results is made with some video quality models using “LIVE”, “IVP” and “IRCCyN/IVC 1080i” databases. The performance metrics, i.e. Pearson and Spearman correlation coefficients, indicate that the proposed method gives a good performance in H264 and MPEG2 codec distortions with the three databases comparing with other models.

Efficient Image Sharpness Assessment Based on Content Aware Total Variation

State-of-the-art sharpness assessment methods are mostly based on edge width, gradient, high-frequency energy, or pixel intensity variation. Such methods consider very little the image content variation in conjunction with the sharpness assessment which causes the sharpness metric to be less effective for different content images. In this paper, we propose an efficient no-reference image sharpness assessment called content aware total variation (CATV) by considering the importance of image content variation in sharpness measurement. By parameterizing the image TV statistics using generalized Gaussian distribution, the sharpness measure is identified by the standard deviation, and the image content variation evaluator is indicated by the shape parameter. However, the standard deviation is content-dependent which is different for the regions with strong edges, high frequency textures, low frequency textures, and blank areas. By incorporating the shape-parameter in moderating of the standard deviation, we propose a content aware sharpness metric. The experimental results show that the proposed method is highly correlated with the human vision system and has better sharpness assessment results than the state-of-the-art techniques on the blurred subset images of LIVE, TID2008, CSIQ, and IVC databases. Also, our method has very low computational complexity which is suitable for online applications. The correlations with the subjective of the four databases and statistical significance analysis reveal that our method has superior results when compared with previous techniques.

Automated computational aberration correction method for broadband interferometric imaging techniques.

Numerical correction of optical aberrations provides an inexpensive and simpler alternative to the traditionally used hardware-based adaptive optics techniques. In this Letter, we present an automated computational aberration correction method for broadband interferometric imaging techniques. In the proposed method, the process of aberration correction is modeled as a filtering operation on the aberrant image using a phase filter in the Fourier domain. The phase filter is expressed as a linear combination of Zernike polynomials with unknown coefficients, which are estimated through an iterative optimization scheme based on maximizing an image sharpness metric. The method is validated on both simulated data and experimental data obtained from a tissue phantom, an ex vivo tissue sample, and an in vivo photoreceptor layer of the human retina.

Adaptive enhancement of compressed SAR images

Synthetic aperture radar (SAR) imagery is an important global all-weather surveillance and mapping satellite imagery system. As space-borne systems have a limited storage capacity, it is imperative to heavily compress SAR images, possible with lossy compression schemes. As a result, SAR images need to be enhanced in earth stations. The work reported in this paper aims to address the issue of compression artefact removal of SAR images in an adaptive manner. The SAR images, compressed using the JPEG utility at significantly low bit rates, are enhanced by adaptively removing coding artefacts and speckle noise. As edges carry significant information in satellite imagery, a significant edge image is used for edge enhancement with selective removal of noisy edges. Further, an image sharpness metric is proposed in this work to serve as an objective no-reference metric for measuring the sharpness of SAR images.

Parameter Estimation of a Ground Moving Target Using Image Sharpness Optimization.

Motion parameter estimation of a ground moving target is an important issue in synthetic aperture radar ground moving target indication (SAR-GMTI) which has significant applications for civilian and military. The SAR image of a moving target may be displaced and defocused due to the radial and along-track velocity components, respectively. The sharpness cost function presents a measure of the degree of focus of the image. In this work, a new ground moving target parameter estimation algorithm based on the sharpness optimization criterion is proposed. The relationships between the quadratic phase errors and the target’s velocity components are derived. Using two-dimensional searching of the sharpness cost function, we can obtain the velocity components of the target and the focused target image simultaneously. The proposed moving target parameter estimation method and image sharpness metrics are analyzed in detail. Finally, numerical results illustrate the effective and superior velocity estimation performance of the proposed method when compared to existing algorithms.

Non-reference assessment of sharpness in blur/noise degraded images

Image sharpness perception is not only affected by blur but also by noise. Noise effect on perceived image sharpness is a puzzling problem since image sharpness may increase, up to a certain amount of noise, on even regions when noise is added to an image. In this paper, we propose a NR perceived sharpness metric GSVD (Gradient Singular Value Decomposition), that shows to be effective in correlating with subjective quality evaluation of images affected by either blur or noise. This metric (i) requires no training on human image quality ratings, (ii) provides comparable performance with full reference (FR) peak signal to noise ratio (PSNR) and multiscale structural similarity (MSSIM), and (iii) performs better than most of the state-of-the-art NR sharpness metrics when assessing quality in blurry image sets and noisy image sets jointly.

Fusion of operators for heterogeneous periocular recognition at varying ranges

Cross-spectral matching of active and passive infrared (IR) periocular images to a visible light periocular image gallery is a challenging research problem. This scenario is motivated by a number of surveillance applications such as recognition of subjects at night or in harsh environmental conditions. This problem becomes even more challenging with a varying standoff distance. To address this problem a new compound operator named GWLH that fuses three local descriptors – Histogram of Gradients (HOG), Local Binary Patterns (LBP) and Weber Local Descriptors (WLD) – applied to the outputs of Gabor filters is proposed. The local operators encode both magnitude and phase information. When applied to periocular regions, GWLH outperforms other compound operators that recently appeared in the literature. During performance evaluation LBP, Gabor filters, HOG, and a fusion of HOG and LBP establish a baseline for the performance comparison, while other compound operators such as Gabor followed by HOG and LBP as well as Gabor followed by WLD, LBP and GLBP present the state-of-the-art. The active IR band is presented by short-wave infrared (SWIR) and near-infrared (NIR) and passive IR is presented by mid-wave infrared (MWIR) and long-wave infrared (LWIR). In addition to varying spectrum, we also vary the standoff distance of SWIR and NIR probes. In all but one case of the combination of spectrum and range, GWLH outperforms all the other operators. A sharpness metric is introduced to measure the quality of heterogeneous periocular images and to emphasize the need in development of image enhancement approaches for heterogeneous periocular biometrics. Based on the statistics of the sharpness metric, the performance difference between compound and single operators is increasing proportionally with increasing sharpness metric values.