Image Noise Level Research Articles

Estimation of the Scanning Speed of Random Texture by a Multirow Focal Plane Array

Scanning devises based on a set of mutually shifted focal plane arrays to increase the sampling rate in a direction orthogonal to the scanning direction require a high degree of stabilization of the scanning speed in order to obtain high-quality images. A new method for estimating the scanning speed is proposed based on the analysis of shifts of arbitrary random texture images generated by subarrays during scanning. The dependence of the accuracy of various methods of estimating the scanning speed based on the statistical characteristics of the texture is studied by simulation modeling. It is shown that even at a sufficiently high random noise level (SNR = 10) in images with texture satisfying the Nyquist criterion for each subarray, scanning speed estimation errors do not exceed 0.1% of the nominal value.

Discriminative Transfer Learning for General Image Restoration.

Recently, several discriminative learning approaches have been proposed for effective image restoration, achieving convincing trade-off between image quality and computational efficiency. However, these methods require separate training for each restoration task (e.g., denoising, deblurring, demosaicing) and problem condition (e.g., noise level of input images). This makes it time-consuming and difficult to encompass all tasks and conditions during training. In this paper, we propose a discriminative transfer learning method that incorporates formal proximal optimization and discriminative learning for general image restoration. The method requires a single-pass discriminative training and allows for reuse across various problems and conditions while achieving an efficiency comparable to previous discriminative approaches. Furthermore, after being trained, our model can be easily transferred to new likelihood terms to solve untrained tasks, or be combined with existing priors to further improve image restoration quality.

Determination of a suitable low‐dose abdominopelvic CT protocol using model‐based iterative reconstruction through cadaveric study

Cadaveric studies provide a means of safely assessing new technologies and optimizing scanning prior to clinical validation. Reducing radiation exposure in a clinical setting can entail incremental dose reductions to avoid missing important clinical findings. The use of cadavers allows assessment of the impact of more substantial dose reductions on image quality. Our aim was to identify a suitable low-dose abdominopelvic CT protocol for subsequent clinical validation. Five human cadavers were scanned at one conventional dose and three low-dose settings. All scans were reconstructed using three different reconstruction algorithms: filtered back projection (FBP), hybrid iterative reconstruction (60% FBP and 40% adaptive statistical iterative reconstruction (ASIR40)), and model-based iterative reconstruction (MBIR). Two readers rated the image quality both quantitatively and qualitatively. Model-based iterative reconstruction images had significantly better objective image noise and higher qualitative scores compared with both FBP and ASIR40 images at all dose levels. The greatest absolute noise reduction, between MBIR and FBP, of 34.3 HU (equating to a 68% reduction) was at the lowest dose level. MBIR reduced image noise and improved image quality even in CT images acquired with a mean radiation dose reduction of 62% compared with conventional dose studies reconstructed with ASIR40, with lower levels of objective image noise, superior diagnostic acceptability and contrast resolution, and comparable subjective image noise and streak artefact scores. This cadaveric study demonstrates that MBIR reduces image noise and improves image quality in abdominopelvic CT images acquired with dose reductions of up to 62%.

Low-dose CT with adaptive statistical iterative reconstruction for evaluation of urinary stone.

PurposeTo prospectively determine the diagnostic performance of low-dose CT (LDCT) with adaptive statistical iterative reconstruction (ASIR) technique for the detection of urinary stone disease.ResultsThe average DLP and ED was 408.16 ± 119.04 mGy and 6.12 ± 1.79 mSv in CDCT, and 138.19 ± 76.87 mGy and 2.07 ± 1.15 mSv in LDCT, respectively. The dose reduction rate of LDCT was nearly 66.1% for both DLP and ED (P < 0.05). LDCT–80% ASIR images showed great image quality (mean score = 4.09), which was similar to CDCT-FBP images (mean score = 4.17) (P > 0.05), but higher than LDCT-FBP images (mean score = 2.77) (P < 0.05).Materials and Methods70 consetutive patients with clinically suspected urolithiasis underwent non-enhanced CT. Followed by both conventional-dose CT (CDCT) and low-dose CT (LDCT) scans. Automatic tube current modulation (ATCM) scanning was used, with a noise index setting of 13 in CDCT and 25 in LDCT. Reconstructions were performed with filtered back projection (FBP) and different settings of adaptive statistical iterative reconstruction [ASIR(40%, 60%, 80%)]. Urinary calculi (size, location, number), image quality (scale 1–5), image noise (scale 1–3) and diagnostic confidence levels (scale 1–3) were evaluated and measured by two radiologists independently. Radiation dose was recorded by calculating dose length product (DLP) and effective dose (ED). Statistical analyses included Mann-Whitney U test and Paired t tests.ConclusionsLDCT with ASIR can reduce the radiation dose while maintain relatively high image quality in the diagnosis of urinary stone diseases.

On micromechanical parameter identification with integrated DIC and the role of accuracy in kinematic boundary conditions

Integrated Digital Image Correlation (IDIC) is nowadays a well established full-field experimental procedure for reliable and accurate identification of material parameters. It is based on the correlation of a series of images captured during a mechanical experiment, that are matched by displacement fields derived from an underlying mechanical model. In recent studies, it has been shown that when the applied boundary conditions lie outside the employed field of view, IDIC suffers from inaccuracies. A typical example is a micromechanical parameter identification inside a Microstructural Volume Element (MVE), whereby images are usually obtained by electron microscopy or other microscopy techniques but the loads are applied at a much larger scale. For any IDIC model, MVE boundary conditions still need to be specified, and any deviation or fluctuation in these boundary conditions may significantly influence the quality of identification. Prescribing proper boundary conditions is generally a challenging task, because the MVE has no free boundary, and the boundary displacements are typically highly heterogeneous due to the underlying microstructure. The aim of this paper is therefore first to quantify the effects of errors in the prescribed boundary conditions on the accuracy of the identification in a systematic way. To this end, three kinds of mechanical tests, each for various levels of material contrast ratios and levels of image noise, are carried out by means of virtual experiments. For simplicity, an elastic compressible Neo-Hookean constitutive model under plane strain assumption is adopted. It is shown that a high level of detail is required in the applied boundary conditions. This motivates an improved boundary condition application approach, which considers constitutive material parameters as well as kinematic variables at the boundary of the entire MVE as degrees of freedom in the IDIC procedure, assuring that both are identified with equal precision and importance. This problem has been studied in the literature with a different method, i.e. Finite Element Method Updating framework.

Noise Level Estimation for Digital Images Using Local Statistics and Its Applications to Noise Removal

In this paper, an automatic estimation of additive white Gaussian noise technique is proposed. This technique is built according to the local statistics of Gaussian noise. In the field of digital signal processing, estimation of the noise is considered as pivotal process that many signal processing tasks relies on. The main aim of this paper is to design a patch-based estimation technique in order to estimate the noise level in natural images and use it in blind image removal technique. The estimation processes is utilized selected patches which is most contaminated sub-pixels in the tested images sing principal component analysis (PCA). The performance of the suggested noise level estimation technique is shown its superior to state of the art noise estimation and noise removal algorithms, the proposed algorithm produces the best performance in most cases compared with the investigated techniques in terms of PSNR, IQI and the visual perception.

Patch based fast noise level estimation using DCT and standard deviation

Image noise level estimation play a vital role in image processing applications such as medical imaging and celestial imaging. The noise level estimation of image is hard to estimate due to texture of image. Conventional methods segment image in blocks to identify noise in image. The method provides erroneous noise detection in high textured image such as medical images. In this paper, we propose a Patch based DCT (PDCT) model to decompose image in spatial domain in parallel pool loop for medical image slices. The PDCT model split noisy image into patches to exhibit noise in image. The PDCT model estimates noise level accurately in complex images compared to conventional noise level estimation methods such as principle component analysis and weak textured patch methods.

Clinically Acceptable Optimized Dose Reduction in Computed Tomographic Imaging of Necrotizing Pancreatitis Using a Noise Addition Software Tool.

This study aimed to determine potential radiation dose reduction of contrast-enhanced computed tomography (CECT) for imaging necrotizing pancreatitis (NP) using a noise addition tool. Eighty-four patients were identified with at least 1 abdominopelvic CECT for NP within a 2-year period. Sixty consecutive scans were selected as reference radiation dose data sets. A noise addition software was used to simulate 4 data sets of increased noise. Readers rated confidence for identifying (i) anatomic structures, (ii) complications of NP, and (iii) diagnostic acceptability. Noise and dose levels were identified at acceptability threshold where observer scores were statistically indistinguishable from full-dose computed tomographies. Observers' perception of image tasks decreased progressively with increasing noise (P < 0.05). Acceptability and statistical analysis indicated that noise can be increased from 10 to 25 HU corresponding to an 84% reduction in dose without change in observer perception (P < 0.05). Higher image noise levels may be tolerated in CECT in patients with NP.

A Single Radio Source in the High-redshift Dual AGN LBQS 0302-0019

By discovering a narrow-line emitter embedded in an extended Ly-alpha nebula, Husemann et al. (2018) found that the luminous radio-quiet quasar LBQS 0302-0019 is a dual active galactic nucleus (AGN) system. By combining available archival radio interferometric data taken at 8.4 GHz with the Very Large Array (VLA), we found that the weak 0.4-mJy radio source in this system positionally coincides with the unobscured optical quasar while its newly discovered obscured companion has no detectable radio emission (<0.045 mJy/beam, 3-sigma image noise level). However, the non-detection of this object with the VLA does not rule out the presence of a radio-quiet AGN there.

Influence of imaging configurations on the accuracy of digital image correlation measurement

Compared to other approaches to improving digital image correlation (DIC) measurement accuracy, improving image quality is becoming increasingly effective. Image noise level, which is the main indicator of image quality, is very sensitive to the configurations of an imaging system and affects DIC measurement accuracy. However, the mechanism by which the imaging configurations affect accuracy is unclear. In this study, the influence of imaging configuration on DIC measurement accuracy was investigated using the common factor of noise. Firstly, the type and levels of noise introduced into actual images by different imaging system configurations were investigated. Next, the effect of noise on DIC measurement accuracy was determined using simulated images. Finally, the effects of imaging configuration on DIC measurement accuracy were experimentally investigated using actual measurements. The results show that both camera-related and lens-related configurations affect DIC measurement accuracy. In particular, lengthening the exposure time and reducing the f-number of the lens achieve similar improvement in DIC measurement accuracy, and a prime lens with high light transmittance is recommended for full-field optical measurements. In contrast, the imaging gain does not affect the accuracy of DIC measurements.

Improved Opacification of a Suboptimally Enhanced Pulmonary Artery in Chest CT: Experience Using a Dual-Layer Detector Spectral CT.

The objective of our study was to evaluate the quality of virtual monoenergetic imaging (VMI) from dual-layer detector spectral CT and the effect of virtual monoenergetic images obtained at low energies on the detection of pulmonary embolism (PE) in patients with a suboptimally enhanced pulmonary artery on chest CT. Of 1552 consecutive chest CT examinations performed with dual-layer detector spectral CT using a routine protocol with a tube voltage of 120 kVp, 79 examinations with suboptimal enhancement of the pulmonary artery (i.e., mean attenuation of pulmonary artery ≤ 180 HU) were included. The mean attenuation of the pulmonary artery, noise, contrast-to-noise ratio (CNR), and signal-to-noise ratio (SNR) of virtual monoenergetic images obtained at 40-200 keV were compared with those of the conventional 120-kVp images. The virtual monoenergetic images with the best CNR were compared with the 120-kVp images with regard to subjective image quality and diagnostic accuracy for detecting PE. Sufficient attenuation of the pulmonary artery (> 180 HU) was obtained using VMI for 78 of the 79 examinations. The noise levels of the virtual monoenergetic images were gradually increased with decreasing energy level (i.e., kiloelectron volt setting). The CNR and SNR of virtual monoenergetic images at 40-65 keV were significantly higher (both, p < 0.001) than the CNR and SNR of the 120-kVp images. The CNR was the highest at 40 keV for all cases. Diagnostic accuracy for detecting PE was significantly higher for 40-keV images (reader 1: AUC = 0.992, p = 0.033; reader 2: AUC = 0.986, p = 0.043) than for 120-kVp images (reader 1, AUC = 0.911; reader 2, AUC = 0.933). The subjective quality was not different between these two images. In chest CT examinations in which the pulmonary artery is suboptimally enhanced, obtaining virtual monoenergetic images at a low energy setting using dual-layer detector spectral CT allows sufficient attenuation of the pulmonary artery to be achieved while preserving image quality and increasing diagnostic performance for detecting PE.

Deep Shrinkage Convolutional Neural Network for Adaptive Noise Reduction

The noise level of an image depends on settings of an imaging device. The settings can be used to select appropriate parameters for denoising methods. But denoising methods based on deep convolutional neural networks (deep-CNN) do not have such adjustable parameters. Therefore, a deep-CNN whose training data contain limited levels of noise does not effectively restore images whose noise level is different from the training data. If the range of noise levels of training data is extended to solve the problem, the maximum performance of a produced deep-CNN is limited. To solve the tradeoff, we propose a deep-CNN that is adjustable to the noise level of the input image immediately. We use soft shrinkage for activation functions of our deep-CNN. The soft shrinkage has thresholds proportional to the noise level given by the user. We also propose an optimization method for proportionality coefficients for the thresholds of soft shrinkage. Our method optimizes the coefficients for various noise levels simultaneously. In our experiment using a test set whose noise level is from 5 to 50, the proposed method showed higher PSNR than that in the case of the conventional method using only one deep-CNN, and PSNR comparable to that in the case of the conventional method using multiple noise-level-specific CNNs.

Fast noise level estimation algorithm based on principal component analysis transform and nonlinear rectification

We proposed a noniterative principal component analysis (PCA)-based noise level estimation (NLE) algorithm that addresses the problem of estimating the noise level with a two-step scheme. First, we randomly extracted a number of raw patches from a given noisy image and took the smallest eigenvalue of the covariance matrix of the raw patches as the preliminary estimation of the noise level. Next, the final estimation was directly obtained with a nonlinear mapping (rectification) function that was trained on some representative noisy images corrupted with different known noise levels. Compared with the state-of-art NLE algorithms, the experiment results show that the proposed NLE algorithm can reliably infer the noise level and has robust performance over a wide range of image contents and noise levels, showing a good compromise between speed and accuracy in general.

Optimized digital speckle patterns for digital image correlation by consideration of both accuracy and efficiency.

The technique of digital image correlation (DIC), which has been widely used for noncontact deformation measurements in both the scientific and engineering fields, is greatly affected by the quality of speckle patterns in terms of its performance. This study was concerned with the optimization of the digital speckle pattern (DSP) for DIC in consideration of both the accuracy and efficiency. The root-mean-square error of the inverse compositional Gauss-Newton algorithm and the average number of iterations were used as quality metrics. Moreover, the influence of subset sizes and the noise level of images, which are the basic parameters in the quality assessment formulations, were also considered. The simulated binary speckle patterns were first compared with the Gaussian speckle patterns and captured DSPs. Both the single-radius and multi-radius DSPs were optimized. Experimental tests and analyses were conducted to obtain the optimized and recommended DSP. The vector diagram of the optimized speckle pattern was also uploaded as reference.

Fast GPU-based Monte Carlo code for SPECT/CT reconstructions generates improved 177Lu images

BackgroundFull Monte Carlo (MC)-based SPECT reconstructions have a strong potential for correcting for image degrading factors, but the reconstruction times are long. The objective of this study was to develop a highly parallel Monte Carlo code for fast, ordered subset expectation maximum (OSEM) reconstructions of SPECT/CT images. The MC code was written in the Compute Unified Device Architecture language for a computer with four graphics processing units (GPUs) (GeForce GTX Titan X, Nvidia, USA). This enabled simulations of parallel photon emissions from the voxels matrix (1283 or 2563). Each computed tomography (CT) number was converted to attenuation coefficients for photo absorption, coherent scattering, and incoherent scattering. For photon scattering, the deflection angle was determined by the differential scattering cross sections. An angular response function was developed and used to model the accepted angles for photon interaction with the crystal, and a detector scattering kernel was used for modeling the photon scattering in the detector. Predefined energy and spatial resolution kernels for the crystal were used. The MC code was implemented in the OSEM reconstruction of clinical and phantom 177Lu SPECT/CT images. The Jaszczak image quality phantom was used to evaluate the performance of the MC reconstruction in comparison with attenuated corrected (AC) OSEM reconstructions and attenuated corrected OSEM reconstructions with resolution recovery corrections (RRC).ResultThe performance of the MC code was 3200 million photons/s. The required number of photons emitted per voxel to obtain a sufficiently low noise level in the simulated image was 200 for a 1283 voxel matrix. With this number of emitted photons/voxel, the MC-based OSEM reconstruction with ten subsets was performed within 20 s/iteration. The images converged after around six iterations. Therefore, the reconstruction time was around 3 min. The activity recovery for the spheres in the Jaszczak phantom was clearly improved with MC-based OSEM reconstruction, e.g., the activity recovery was 88% for the largest sphere, while it was 66% for AC-OSEM and 79% for RRC-OSEM.ConclusionThe GPU-based MC code generated an MC-based SPECT/CT reconstruction within a few minutes, and reconstructed patient images of 177Lu-DOTATATE treatments revealed clearly improved resolution and contrast.

The Most Compact Bright Radio-loud AGNs. II. VLBA Observations of 10 Sources at 43 and 86 GHz

Abstract Radio-loud active galactic nuclei (AGNs), hosting powerful relativistic jet outflows, provide an excellent laboratory for studying jet physics. Very long baseline interferometry (VLBI) enables high-resolution imaging on milli-arcsecond (mas) and sub-mas scales, making it a powerful tool to explore the inner jet structure, shedding light on the formation, acceleration, and collimation of AGN jets. In this paper, we present Very Long Baseline Array observations of 10 radio-loud AGNs at 43 and 86 GHz that were selected from the Planck catalog of compact sources and are among the brightest in published VLBI images at and below 15 GHz. The image noise levels in our observations are typically 0.3 and 1.5 mJy beam−1 at 43 and 86 GHz, respectively. Compared with the VLBI data observed at lower frequencies from the literature, our observations with higher resolutions (with the highest resolution being up to 0.07 mas at 86 GHz and 0.18 mas at 43 GHz) and at higher frequencies detected new jet components at sub-parsec scales, offering valuable data for studies of the physical properties of the innermost jets. These include the compactness factor of the radio structure (the ratio of core flux density to total flux density), and core brightness temperature ( ). In all these sources, the compact core accounts for a significant fraction ( ) of the total flux density. Their correlated flux density at the longest baselines is higher than 0.16 Jy. The compactness of these sources make them good phase calibrators of millimeter-wavelength ground-based and space VLBI.

Robust denoising technique for ultrasound images by splicing of low rank filter and principal component analysis

Robust image denoising techniques are still a significant challenge for medical ultrasound images. Though the difficulty of the recently proposed strategies, most techniques have not yet achieved desired level of applicability. The principle focus of this paper is a thresholding strategy together with a splicing of Low Rank Filtering (LRF) and Principal Component Analysis (PCA) techniques. By means of the proposed robust denoising technique, the denoised medical ultrasound image is assessed until the point that it reaches desired level. The input noisy medical ultrasound image is given to the hybridization technique of LRF mechanism and PCA transformation which is used for removing the noise in medical ultrasound image. For effective image denoising, the segmentation process is done with the guide of sparse decomposition framework. Consequently, denoised image threshold is compared with the threshold value of training images in the database and this is repeated until we get a better denoised image. For analysis purpose, the set of five medical ultrasound images of artery have been considered. These medical ultrasound images are used for quantitative analysis. Performance of these recent filters as two phase matrix decomposition denoised filter, new adaptive denoised filter, and fast fourier bessel steerable PCA (FFBsPCA) denoised filter have been compared with our proposed splicing denoised method in terms of peak signal to noise ratio (PSNR) value and mean square error (MSE) value performance index under various noise density selection. Analytical results for set of five medical ultrasound images have shown that this proposed splice filtering method is more robust for noise reduction. So, it has shown that proposed splice filtering method is robust for medical ultrasound image denoising which preserves the clinical details and minimizing the noise level in medical ultrasound images.

Noise Robust Face Image Super-Resolution Through Smooth Sparse Representation.

Face image super-resolution has attracted much attention in recent years. Many algorithms have been proposed. Among them, sparse representation (SR)-based face image super-resolution approaches are able to achieve competitive performance. However, these SR-based approaches only perform well under the condition that the input is noiseless or has small noise. When the input is corrupted by large noise, the reconstruction weights (or coefficients) of the input low-resolution (LR) patches using SR-based approaches will be seriously unstable, thus leading to poor reconstruction results. To this end, in this paper, we propose a novel SR-based face image super-resolution approach that incorporates smooth priors to enforce similar training patches having similar sparse coding coefficients. Specifically, we introduce the fused least absolute shrinkage and selection operator-based smooth constraint and locality-based smooth constraint to the least squares representation-based patch representation in order to obtain stable reconstruction weights, especially when the noise level of the input LR image is high. Experiments are carried out on the benchmark FEI face database and CMU+MIT face database. Visual and quantitative comparisons show that the proposed face image super-resolution method yields superior reconstruction results when the input LR face image is contaminated by strong noise.

Towards reliable object detection in noisy images

Image noise is a common problem frequently caused by insufficient lighting, low-quality cameras, image compression and other factors. While low image quality is expected to degrade results of visual recognition, most of the current methods and benchmarks for object recognition, such as Pascal Visual Object Classes Challenge and Microsoft Common Objects in Context Challenge, focus on relatively high-quality images. Meanwhile, object recognition in noisy images is a common problem in surveillance and other domains. In this work we address object detection in noisy images and propose a novel low-cost method for image denoising. When combined with the standard Deformable Parts Model and Regions with Convolutional Neural Network object detectors, our method shows improvements of object detection under varying levels of image noise. We present a comprehensive experimental evaluation and compare our method to other denoising techniques as well as to standard detectors re-trained on noisy images. Results are presented for the common Pascal Visual Object Classes benchmark for object detection and KAIST Multispectral Pedestrian Detection Benchmark with the real noise presence in night images.

Enhancing quality in Diffusion Tensor Imaging with anisotropic anomalous diffusion filter

Abstract Introduction: Diffusion tensor imaging (DTI) is an important medical imaging modality that has been useful to the study of microstructural changes in neurological diseases. However, the image noise level is a major practical limitation, in which one simple solution could be the average signal from a sequential acquisition. Nevertheless, this approach is time-consuming and is not often applied in the clinical routine. In this study, we aim to evaluate the anisotropic anomalous diffusion (AAD) filter in order to improve the general image quality of DTI. Methods A group of 20 healthy subjects with DTI data acquired (3T MR scanner) with different numbers of averages (N=1,2,4,6,8, and 16), where they were submitted to 2-D AAD and conventional anisotropic diffusion filters. The Relative Mean Error (RME), Structural Similarity Index (SSIM), Coefficient of Variation (CV) and tractography reconstruction were evaluated on Fractional Anisotropy (FA) and Apparent Diffusion Coefficient (ADC) maps. Results The results point to an improvement of up to 30% of CV, RME, and SSIM for the AAD filter, while up to 14% was found for the conventional AD filter (p<0.05). The tractography revealed a better estimative in fiber counting, where the AAD filter resulted in less FA variability. Furthermore, the AAD filter showed a quality improvement similar to a higher average approach, i.e. achieving an image quality equivalent to what was seen in two additional acquisitions. Conclusions In general, the AAD filter showed robustness in noise attenuation and global image quality improvement even in DTI images with high noise level.