Stripe Artifacts Research Articles

Denoise diffusion-weighted images using higher-order singular value decomposition

Noise usually affects the reliability of quantitative analysis in diffusion-weighted (DW) magnetic resonance imaging (MRI), especially at high b-values and/or high spatial resolution. Higher-order singular value decomposition (HOSVD) has recently emerged as a simple, effective, and adaptive transform to exploit sparseness within multidimensional data. In particular, the patch-based HOSVD denoising has demonstrated superb performance when applied to T1-, T2-, and proton density-weighted MRI data. In this study, we aim to investigate the feasibility of denoising DW data using the HOSVD transform. With the low signal-to-noise ratio in typical DW data, the patch-based HOSVD denoising suffers from stripe artifacts in homogeneous regions because of the HOSVD bases learned from the noisy patches. To address this problem, we propose a novel denoising method. It first introduces a global HOSVD-based denoising as a prefiltering stage to guide the subsequent patch-based HOSVD denoising stage. The HOSVD bases from the patch groups in prefiltered images are then used to transform the noisy patch groups in original DW data. Experiments were performed using simulated and in vivo DW data. Results show that the proposed method significantly reduces stripe artifacts compared with conventional patch-based HOSVD denoising methods, and outperforms two state-of-the-art denoising methods in terms of denoising quality and diffusion parameters estimation.

A Ring Artifact Correction Method: Validation by Micro-CT Imaging with Flat-Panel Detectors and a 2D Photon-Counting Detector.

We introduce an efficient ring artifact correction method for a cone-beam computed tomography (CT). In the first step, we correct the defective pixels whose values are close to zero or saturated in the projection domain. In the second step, we compute the mean value at each detector element along the view angle in the sinogram to obtain the one-dimensional (1D) mean vector, and we then compute the 1D correction vector by taking inverse of the mean vector. We multiply the correction vector with the sinogram row by row over all view angles. In the third step, we apply a Gaussian filter on the difference image between the original CT image and the corrected CT image obtained in the previous step. The filtered difference image is added to the corrected CT image to compensate the possible contrast anomaly that may appear due to the contrast change in the sinogram after removing stripe artifacts. We applied the proposed method to the projection data acquired by two flat-panel detectors (FPDs) and a silicon-based photon-counting X-ray detector (PCXD). Micro-CT imaging experiments of phantoms and a small animal have shown that the proposed method can greatly reduce ring artifacts regardless of detector types. Despite the great reduction of ring artifacts, the proposed method does not compromise the original spatial resolution and contrast.

Removing ring artifacts in CBCT images via L0 smoothing

Ring artifacts often appear in cone-beam computed tomography CBCT images due to the inconsistent response of detector pixels. How to remove ring artifacts without impairing the image quality is still a hard problem. This article proposes a novel method to remove ring artifacts from CBCT images. First, the reconstructed CBCT image is transformed from Cartesian coordinates into polar coordinates, such that ring artifacts in Cartesian coordinates are transformed as stripe artifacts in polar coordinates that are easier to be removed. Second, a minimization model based on L0 smoothing is introduced to smooth the transformed image such that the major edges of the image are reserved while the artifacts are eliminated as special low-amplitude structures. Finally, the obtained artifacts are transformed back into Cartesian coordinates and are subtracted from the original CBCT image. Thus, the CBCT image without ring artifacts is obtained. The experiments based on different CBCT images show that the proposed method is more effective compared with other algorithms. © 2016 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 26, 284-294, 2016

Spatio-Temporal Error Sources Analysis and Accuracy Improvement in Landsat 8 Image Ground Displacement Measurements

Because of the advantages of low cost, large coverage and short revisit cycle, Landsat 8 images have been widely applied to monitor earth surface movements. However, there are few systematic studies considering the error source characteristics or the improvement of the deformation field accuracy obtained by Landsat 8 image. In this study, we utilize the 2013 Mw 7.7 Balochistan, Pakistan earthquake to analyze error spatio-temporal characteristics and elaborate how to mitigate error sources in the deformation field extracted from multi-temporal Landsat 8 images. We found that the stripe artifacts and the topographic shadowing artifacts are two major error components in the deformation field, which currently lack overall understanding and an effective mitigation strategy. For the stripe artifacts, we propose a small spatial baseline (<200 m) method to avoid the stripe artifacts effect on the deformation field. We also propose a small radiometric baseline method to reduce the topographic shadowing artifacts and radiometric decorrelation noises. Those performances and accuracy evaluation show that these two methods are effective in improving the precision of deformation field. This study provides the possibility to detect subtle ground movement with higher precision caused by earthquake, melting glaciers, landslides, etc., with Landsat 8 images. It is also a good reference for error source analysis and corrections in deformation field extracted from other optical satellite images.

Stripe artifact elimination based on nonsubsampled contourlet transform for light sheet fluorescence microscopy.

Stripe artifacts, caused by high-absorption or high-scattering structures in the illumination light path, are a common drawback in both unidirectional and multidirectional light sheet fluorescence microscopy (LSFM), significantly deteriorating image quality. To circumvent this problem, we present an effective multidirectional stripe remover (MDSR) method based on nonsubsampled contourlet transform (NSCT), which can be used for both unidirectional and multidirectional LSFM. In MDSR, a fast Fourier transform (FFT) filter is designed in the NSCT domain to shrink the stripe components and eliminate the noise. Benefiting from the properties of being multiscale and multidirectional, MDSR succeeds in eliminating stripe artifacts in both unidirectional and multidirectional LSFM. To validate the method, MDSR has been tested on images from a custom-made unidirectional LSFM system and a commercial multidirectional LSFM system, clearly demonstrating that MDSR effectively removes most of the stripe artifacts. Moreover, we performed a comparative experiment with the variational stationary noise remover and the wavelet-FFT methods and quantitatively analyzed the results with a peak signal-to-noise ratio, showing an improved noise removal when using the MDSR method.

Removing Beam Current Artifacts in Helium Ion Microscopy: A Comparison of Image Processing Techniques.

The development of the helium ion microscope (HIM) enables the imaging of both hard, inorganic materials and soft, organic or biological materials. Advantages include outstanding topographical contrast, superior resolution down to <0.5 nm at high magnification, high depth of field, and no need for conductive coatings. The instrument relies on helium atom adsorption and ionization at a cryogenically cooled tip that is atomically sharp. Under ideal conditions this arrangement provides a beam of ions that is stable for days to weeks, with beam currents in the order of picoamperes. Over time, however, this stability is lost as gaseous contamination builds up in the source region, leading to adsorbed atoms of species other than helium, which ultimately results in beam current fluctuations. This manifests itself as horizontal stripe artifacts in HIM images. We investigate post-processing methods to remove these artifacts from HIM images, such as median filtering, Gaussian blurring, fast Fourier transforms, and principal component analysis. We arrive at a simple method for completely removing beam current fluctuation effects from HIM images while maintaining the full integrity of the information within the image.

JPSS-1 VIIRS Pre-Launch Response Versus Scan Angle Testing and Performance

The Visible Infrared Imaging Radiometer Suite (VIIRS) instruments on-board both the Suomi National Polar-orbiting Partnership (S-NPP) and the first Joint Polar Satellite System (JPSS-1) spacecraft, with launch dates of October 2011 and December 2016 respectively, are cross-track scanners with an angular swath of ±56.06°. A four-mirror Rotating Telescope Assembly (RTA) is used for scanning combined with a Half Angle Mirror (HAM) that directs light exiting from the RTA into the aft-optics. It has 14 Reflective Solar Bands (RSBs), seven Thermal Emissive Bands (TEBs) and a panchromatic Day Night Band (DNB). There are three internal calibration targets, the Solar Diffuser, the BlackBody and the Space View, that have fixed scan angles within the internal cavity of VIIRS. VIIRS has calibration requirements of 2% on RSB reflectance and as tight as 0.4% on TEB radiance that requires the sensor’s gain change across the scan or Response Versus Scan angle (RVS) to be well quantified. A flow down of the top level calibration requirements put constraints on the characterization of the RVS to 0.2%–0.3% but there are no specified limitations on the magnitude of response change across scan. The RVS change across scan angle can vary significantly between bands with the RSBs having smaller changes of ~2% and some TEBs having ~10% variation. Within a band, the RVS has both detector and HAM side dependencies that vary across scan. Errors in the RVS characterization will contribute to image banding and striping artifacts if their magnitudes are above the noise level of the detectors. The RVS was characterized pre-launch for both S-NPP and JPSS-1 VIIRS and a comparison of the RVS curves between these two sensors will be discussed.

Using the robust principal component analysis algorithm to remove RF spike artifacts from MR images.

PurposeBrief bursts of RF noise during MR data acquisition (“k‐space spikes”) cause disruptive image artifacts, manifesting as stripes overlaid on the image. RF noise is often related to hardware problems, including vibrations during gradient‐heavy sequences, such as diffusion‐weighted imaging. In this study, we present an application of the Robust Principal Component Analysis (RPCA) algorithm to remove spike noise from k‐space. Methods: Corrupted k‐space matrices were decomposed into their low‐rank and sparse components using the RPCA algorithm, such that spikes were contained within the sparse component and artifact‐free k‐space data remained in the low‐rank component. Automated center refilling was applied to keep the peaked central cluster of k‐space from misclassification in the sparse component. Results: This algorithm was demonstrated to effectively remove k‐space spikes from four data types under conditions generating spikes: (i) mouse heart T1 mapping, (ii) mouse heart cine imaging, (iii) human kidney diffusion tensor imaging (DTI) data, and (iv) human brain DTI data. Myocardial T1 values changed by 86.1 ± 171 ms following despiking, and fractional anisotropy values were recovered following despiking of DTI data. Conclusion: The RPCA despiking algorithm will be a valuable postprocessing method for retrospectively removing stripe artifacts without affecting the underlying signal of interest. Magn Reson Med 75:2517–2525, 2016. © 2015 The Authors. Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Image-Quality Enhancement for a Holographic Wavefront Color Printer by Adaptive SLM Partitioning

The wavefront printer records a volume-reflection hologram as a two-dimensional array of elemental holograms from computer-generated holograms (CGHs) displayed on a spatial light modulator (SLM). The wavefront coming from the object is extracted by filtering in the spatial-frequency domain. This paper presents a method to improve color reproduction in a wavefront printer with spatial division of exposures at primary colors, by adaptive partitioning of the SLM in accordance with the color content encoded in the input CGHs, and by the controllable change of exposure times for the recording of primary colors. The method is verified with a color wavefront printer with demagnification of the object beam. The quality of reconstruction achieved by the proposed method proves its efficiency in eliminating the stripe artifacts that are superimposed on reconstructed images in conventional mosaic recording.

Radiometric Quality Evaluation of INSAT-3D Imager Data

Abstract. INSAT-3D is an advanced meteorological satellite of ISRO which acquires imagery in optical and infra-red (IR) channels for study of weather dynamics in Indian sub-continent region. In this paper, methodology of radiometric quality evaluation for Level-1 products of Imager, one of the payloads onboard INSAT-3D, is described. Firstly, overall visual quality of scene in terms of dynamic range, edge sharpness or modulation transfer function (MTF), presence of striping and other image artefacts is computed. Uniform targets in Desert and Sea region are identified for which detailed radiometric performance evaluation for IR channels is carried out. Mean brightness temperature (BT) of targets is computed and validated with independently generated radiometric references. Further, diurnal/seasonal trends in target BT values and radiometric uncertainty or sensor noise are studied. Results of radiometric quality evaluation over duration of eight months (January to August 2014) and comparison of radiometric consistency pre/post yaw flip of satellite are presented. Radiometric Analysis indicates that INSAT-3D images have high contrast (MTF &gt; 0.2) and low striping effects. A bias of &lt;4K is observed in the brightness temperature values of TIR-1 channel measured during January–August 2014 indicating consistent radiometric calibration. Diurnal and seasonal analysis shows that Noise equivalent differential temperature (NEdT) for IR channels is consistent and well within specifications.

The Spectral Response of the Landsat-8 Operational Land Imager

Abstract: This paper discusses the pre-launch spectral characterization of the Operational Land Imager (OLI) at the component, assembly and instrument levels and relates results of those measurements to artifacts observed in the on-orbit imagery. It concludes that the types of artifacts observed and their magnitudes are consistent with the results of the pre-launch characterizations. The OLI in-band response was characterized both at the integrated instrument level for a sampling of detectors and by an analytical stack-up of component measurements. The out-of-band response was characterized using a combination of Focal Plane Module (FPM) level measurements and optical component level measurements due to better sensitivity. One of the challenges of a pushbroom design is to match the spectral responses for all detectors so that images can be flat-fielded regardless of the spectral nature of the targets in the imagery. Spectral variability can induce striping (detector-to-detector variation), banding (FPM-to-FPM variation) and other artifacts in the final data products. Analyses of the measured spectral response showed that the maximum discontinuity between FPMs due to spectral filter differences is 0.35% for selected targets for all bands except for Cirrus, where there is almost no signal. The average discontinuity between FPMs is 0.12% for the same targets. These results were expected and are in accordance with the OLI requirements. Pre-launch testing identified low levels (within requirements) of spectral crosstalk amongst the three HgCdTe (Cirrus, SWIR1 and SWIR2) bands of the OLI and on-orbit data confirms this crosstalk in the imagery. Further post-launch analyses and simulations revealed that the strongest crosstalk effect is from the SWIR1 band to the Cirrus band; about 0.2% of SWIR1 signal leaks into the Cirrus. Though the total crosstalk signal is only a few counts, it is evident in some scenes when the in-band cirrus signal is very weak. In moist cirrus-free atmospheres and over typical land surfaces, at least 30% of the cirrus signal was due to the SWIR1 band. In the SWIR1 and SWIR2 bands, crosstalk accounts for no more than 0.15% of the total signal.

Vertically scanned laser sheet microscopy.

Laser sheet microscopy is a widely used imaging technique for imaging the three-dimensional distribution of a fluorescence signal in fixed tissue or small organisms. In laser sheet microscopy, the stripe artifacts caused by high absorption or high scattering structures are very common, greatly affecting image quality. To solve this problem, we report here a two-step procedure which consists of continuously acquiring laser sheet images while vertically displacing the sample, and then using the variational stationary noise remover (VSNR) method to further reduce the remaining stripes. Images from a cleared murine colon acquired with a vertical scan are compared with common stitching procedures demonstrating that vertically scanned light sheet microscopy greatly improves the performance of current light sheet microscopy approaches without the need for complex changes to the imaging setup and allows imaging of elongated samples, extending the field of view in the vertical direction.

Radiometric Calibration Methodology of the Landsat 8 Thermal Infrared Sensor

The science-focused mission of the Landsat 8 Thermal Infrared Sensor (TIRS) requires that it have an accurate radiometric calibration. A calibration methodology was developed to convert the raw output from the instrument into an accurate at-aperture radiance. The methodology is based on measurements obtained during component-level and instrument-level characterization testing. The radiometric accuracy from the pre-flight measurements was estimated to be approximately 0.7%. The calibration parameters determined pre-flight were updated during the post-launch checkout period by utilizing the on-board calibration sources and Earth scene data. These relative corrections were made to adjust for differences between the pre-flight and the on-orbit performance of the instrument, thereby correcting large striping artifacts observed in Earth imagery. Despite this calibration correction, banding artifacts (low frequency variation in the across-track direction) have been observed in certain uniform Earth scenes, but not in other uniform scenes. In addition, the absolute calibration performance determined from vicarious measurements have revealed a time-varying error to the absolute radiance reported by TIRS. These issues were determined to not be caused by the calibration process developed for the instrument. Instead, an investigation has revealed that stray light is affecting the recorded signal from the Earth. The varying optical stray light effect is an ongoing subject of evaluation and investigation, and a correction strategy is being devised that will be added to the calibration process.

SU‐C‐213CD‐01: Automated Marker Tracking for Intra‐Fractional Image Guidance in Radiotherapy

Purpose: We have developed two automated methods for real‐time detection of intra‐fractional target motion during treatment with the aid of metallic markers based on ‘in‐line’ acquired x‐ray images. Fast detection algorithms are desirable to facilitate tumor tracking applications. Methods: A Siemens linac was equipped with a 16×16″ a‐Si flat panel detector (FPD) underneath the treatment head. A diagnostic x‐ray tube was positioned opposite to the treatment head; geometrically separating MV treatment and kV imaging fields on the FPD. We analyzed x‐ray images of a lung phantom equipped with a marker for better contrast. For the automated marker detection we developed two algorithms as part of our image acquisition application. Both algorithms are based on gray‐value template matching with normalized cross‐correlation (NCC). One of the algorithms pre‐ calculates suitable candidate regions by employing a canny edge detection filter. In this study, we wanted to a) study the quality of template matching under the influence of MV induced image artifacts (e.g. stripe artifacts), and b) investigate if pre‐selection of candidate regions leads to reliable marker detection. Results: Our lung‐phantom study has demonstrated that matching a 21×21 pixel template over an 81×81 image with NCC takes about 9.9+−4.5 ms. This method is very reliable and works for a wide dose range. Using canny pre‐filtering reduces the latency to 1.1+−0.7 ms, but marker detection becomes less reliable at our lowest dose level (0.6 mAs). In the presence of an 8×8 cm̂2 MV field, the latencies remain unchanged but the correlation factors decrease sharply by up to 22%. We observed that MV induced stripe artifacts overlapping with the marker are the greatest hurdle to successful detection. Conclusions: While template‐matching over the entire image reliably finds the correct marker position, it is possible to accelerate the search for high contrast images with the help of canny filtering. Supported by Siemens Healthcare/CR.

An improved method for the removal of ring artifacts in high resolution CT imaging

Abstract In high resolution computed tomography (CT) using flat panel detectors, imperfect or defected detector elements cause stripe artifacts in sinogram which results in concentric ring artifacts in the image. Such ring artifacts obscure image details in the regions of interest of the image. In this article, novel techniques are proposed for the detection, classification, and correction of ring artifacts in the sinogram domain. The proposed method is suitable for multislice CT with parallel or fan beam geometry. It can also be employed for ring artifact removal in 3D cone beam volume CT by adopting a sinogram by sinogram processing technique. The detection algorithm is based on applying data driven thresholds on the mean curve and difference curve of the sinogram. The ring artifacts are classified into three types and a separate correction algorithm is used for each class. The performance of the proposed techniques is evaluated on a number of real micro-CT images. Experimental results corroborate that the proposed algorithm can remove ring artifacts from micro-CT images more effectively as compared to other recently reported techniques in the literature.

Non-linear regularized phase retrieval for unidirectional X-ray differential phase contrast radiography

Phase retrieval from unidirectional radiographic differential phase contrast images requires integration of noisy data. A method is presented, which aims to suppress stripe artifacts arising from direct image integration. It is purely algorithmic and therefore, compared to alternative approaches, neither additional alignment nor an increased scan time is required. We report on the theory of this method and present results using numerical as well as experimental data. The method shows significant improvements on the phase retrieval accuracy and enhances contrast in the phase image. Due to its general applicability, the proposed method provides a valuable tool for various 2D imaging applications using differential data.

De-striping hyperspectral imagery using wavelet transform and adaptive frequency domain filtering

Hyperspectral imagers are built line-by-line similar to images acquired by pushbroom sensors. They can experience striping artifacts due to variations in detector response to incident imagery. In this research, a method for hyperspectral image de-striping based on wavelet analysis and adaptive Fourier zero-frequency amplitude normalization has been developed. The algorithm was tested against three other de-striping algorithms. Hyperspectral image bands of different scenes with significant striping and random noise, as well as an image with simulated noise, were used in the testing. The results were assessed visually and quantitatively using frequency domain Signal-to-Noise Ratio (SNR), Root Mean Square Error (RMSE) and/or Peak Signal-to-Ratio (PSNR). The results demonstrated the superiority of our proposed algorithm in de-striping hyperspectral images without introducing unwanted artifacts, yet preserving image details. In the noise-induced image results, the proposed method reduced RMSE error and improved PSNR by 3.5 dB which is better than other tested methods. A Combined method, integrating the proposed algorithm with a generic wavelet-based de-noising algorithm, showed significant random noise suppression in addition to stripe reduction with a PSNR value of 4.3 dB. These findings make the algorithm a candidate for practical implementation on remote sensing images including high resolution hyperspectral images contaminated with stripe and random noise.

Removal of streaking artefact in the images of the Pierre Auger Observatory infra-red cameras

In this paper the problem of removing concentric semi-circular stripe artefacts induced by the operating hardware of the infra red cameras of the Pierre Auger Observatory, is tackled. The method builds on top of a recent algorithm for the removal of artefacts, which presents a robust filter, obtained as a combination of Wavelet and Fourier analysis, capable of eliminating horizontal and vertical stripes in images, while trying to preserve structural features and quantitative values of the image. The method requires several parameters which have been tuned by an exhaustive test on a large set of images. The results show that the method is capable to satisfactorily remove the stripe artefacts.

Artefact reduction for cell migration visualization using spectral domain optical coherence tomography

Visualization of cell migration during chemotaxis using spectral domain optical coherence tomography (OCT) requires non-standard processing techniques. Stripe artefacts and camera noise floor present in OCT data prevent detailed computer-assisted reconstruction and quantification of cell locomotion. Furthermore, imaging artefacts lead to unreliable results in automated texture based cell analysis. Here we characterize three pronounced artefacts that become visible when imaging sample structures with high dynamic range, e.g. cultured cells: (i) time-varying fixed-pattern noise; (ii) stripe artefacts generated by background estimation using tomogram averaging; (iii) image modulations due to spectral shaping. We evaluate techniques to minimize the above mentioned artefacts using an 800 nm optical coherence microscope. Effect of artefact reduction is shown exemplarily on two cell cultures, i.e. Dictyostelium on nitrocellulose substrate, and retinal ganglion cells (RGC-5) cultured on a glass coverslip. Retinal imaging also profits from the proposed processing techniques.

An improved approach based on Moment Matching to Destriping for Hyperion data

Hyperion data are always contaminated by serious vertical striping artefacts, which obscure the true radiation information in the images and reduce the accuracy of Hyperspectral images, therefore, the stripped columns must be corrected before performing further processing and quantitative analyses. This paper introduces the principle of moment matching method, emphasizes the analysis of the formative reason for “edge effect”, and then proposes an improved approach to destripe Hyperion data. Two other methods for destriping have been introduced as well in this paper to compare with the proposed method from both visual effect and quantitative assessment. It turns out that the improved method based on moment matching could achieve the greatest effect for destriping Hyperion data.