Sidelobe Artifacts Research Articles

Enhancing image reconstruction in photoacoustic imaging using spatial coherence mean-to-standard-deviation factor beamforming

In photoacoustic imaging (PAI), a delay-and-sum (DAS) beamforming reconstruction algorithm is widely used due to its ease of implementation and fast execution. However, it is plagued by issues such as high sidelobe artifacts and low contrast, that significantly hinder the ability to differentiate various structures in the reconstructed images. In this study, we propose an adaptive weighting factor called spatial coherence mean-to-standard deviation factor (scMSF) in DAS, which is extended into the spatial frequency domain. By combining scMSF with a minimum variance (MV) algorithm, the clutter level is reduced, thereby enhancing the image contrast. Quantitative results obtained from the phantom experiment demonstrate that our proposed method improves contrast ratio (CR) by 30.15 dB and signal-to-noise ratio (SNR) by 8.62 dB compared to DAS while also improving full-width at half maxima (FWHM) by 56%. From the in-vivo experiments, the scMSF-based reconstruction image exhibits a higher generalized contrast-to-noise ratio (gCNR), indicating improved target detectability with a 25.6% enhancement over DAS and a 22.5% improvement over MV.

Enhancing Row-column array (RCA)-based 3D ultrasound vascular imaging with spatial-temporal similarity weighting.

Ultrasound vascular imaging (UVI) is a valuable tool for monitoring the physiological states and evaluating the pathological diseases. Advancing from conventional two-dimensional (2D) to three-dimensional (3D) UVI would enhance the vasculature visualization, thereby improving its reliability. Row-column array (RCA) has emerged as a promising approach for cost-effective ultrafast 3D imaging with a low channel count. However, ultrafast RCA imaging is often hampered by high-level sidelobe artifacts and low signal-to-noise ratio (SNR), which makes RCA-based UVI challenging. In this study, we propose a spatial-temporal similarity weighting (St-SW) method to overcome these challenges by exploiting the incoherence of sidelobe artifacts and noise between datasets acquired using orthogonal transmissions. Simulation, in vitro blood flow phantom, and in vivo experiments were conducted to compare the proposed method with existing orthogonal plane wave imaging (OPW), row-column-specific frame-multiply-and-sum beamforming (RC-FMAS), and XDoppler techniques. Qualitative and quantitative results demonstrate the superior performance of the proposed method. In simulations, the proposed method reduced the sidelobe level by 31.3 dB, 20.8 dB, and 14.0 dB, compared to OPW, XDoppler, and RC-FMAS, respectively. In the blood flow phantom experiment, the proposed method significantly improved the contrast-to-noise ratio (CNR) of the tube by 26.8 dB, 25.5 dB, and 19.7 dB, compared to OPW, XDoppler, and RC-FMAS methods, respectively. In the human submandibular gland experiment, it not only reconstructed a more complete vasculature but also improved the CNR by more than 15 dB, compared to OPW, XDoppler, and RC-FMAS methods. In summary, the proposed method effectively suppresses the side-lobe artifacts and noise in images collected using an RCA under low SNR conditions, leading to improved visualization of 3D vasculatures.

Fast sparse Bayesian learning-based seismic resolution enhancement

Seismic high resolution processing plays a crucial role in seismic interpretation and reservoir characterization. However, the low and high frequency of seismic signal are lost owing to the effects of earth filtering and diffraction. The missing of low-frequency increases the energy of wavelet side lobe, which not only enhances the interference with adjacent events, but also lead to side lobe artifacts. High frequency attenuation is more serious, which significantly reduces the seismic resolution. In addition, the current resolution enhancement methods are difficult to achieve desirable result in noise contaminated environment. A novel resolution enhancement method that extends high and low frequencies simultaneously and improves the signal-to-noise ratio (SNR) under the framework of sparse decomposition theory is proposed. Firstly, an over-complete dictionary is constructed using non-zero phase Ricker wavelet and then the effective seismic signal is extracted by sparse decomposition using the fast sparse Bayesian learning (FSBL) algorithm. Secondly, the effective frequency range of the seismic signal is determined through a series of atoms obtained by sparse decomposition. Then, a desired broadband spectrum can be identified depending on this range. By approximating the broadband spectrum of seismic data, the weak high-frequency information can be extended. Furthermore, a correction term is introduced in frequency domain to suppress the side lobes of atoms. The side lobes are well suppressed without distorting the main lobe, meanwhile, both the low and high frequencies are extended. Finally, the validity of this method is verified by synthetic and field data. The results show that this method has better performance than the ordinary deconvolution and spectral whitening methods in processing high resolution data.

Multi-shaping sparse-continuous reconstruction for an optical coherence tomography sidelobe suppression.

Optical coherence tomography (OCT) images are commonly affected by sidelobe artifacts due to spectral non-uniformity and spectral leakage. Conventional frequency domain spectral shaping methods widen the mainlobe and compromise axial resolution. While image-domain deconvolution techniques can address the trade-off between axial resolution and artifact suppression, their reconstruction quality relies on accurate measurement or estimation of system point spread function (PSF). Inaccurate PSF estimation leads to loss of details in the reconstructed images. In this Letter, we introduce multi-shaping sparse-continuous reconstruction (MSSCR) for an OCT image, a novel, to the best of our knowledge, framework that combines spectral multi-shaping and iterative image reconstruction with sparse-continuous priors. The MSSCR achieves sidelobe suppression without requiring any PSF measurement or estimation and effectively preserving the axial resolution. The experimental results demonstrate that the MSSCR achieves sidelobe suppression of more than 8 dB. We believe that the MSSCR holds potential for addressing sidelobe artifacts in OCT.

Unveiling the bathypelagic zone with an acoustic vertical profiler

There is a growing interest in the study of mesopelagic and bathypelagic species due to their implication in the active carbon flux and climate change. Underwater acoustics is one of the most efficient methods to study such widely distributed communities. However, there are several challenges among which the reduced spatial resolution with depth for vessel-mounted echosounders stands out. The increase in sampling volume with range precludes observation of fine structures within the scattering layers or the study of less-aggregated organisms far from the transducer face. Echosounders are thus increasingly being deployed in alternative platforms that get the transducer close to the layers of interest. We present here the comparison between two active acoustic platforms used to analyse mesopelagic and bathypelagic species: a vessel-mounted EK60 scientific echosounder and a 6000 m depth rated Acoustic Zooplankton Fish Profiler (AZFP) attached to a CTD rosette. Both systems depicted similar vertical distributions and scattering at 38 kHz for the main scattering layers recorded around the Canary Islands but the finer vertical resolution of the AZFP discerned a series of sub-layers within them that differed in scattering, avoidance and sparsity. The availability of high frequencies at depth also provided further insights on the frequency response of those layers. However, the horizontal orientation of the AZFP created sidelobe artefacts close to the surface and bottom that limit its use in those areas. Signal-to-noise ratio of the AZFP system in the bathypelagic zone also precludes applying echointegration analysis at those depths or outside the denser scattering layers. Finally, the visualization of horizontal or temporal patterns such as migration or mesoscale features is limited to a coarse resolution using data from closely spaced successive stations. New challenges encountered with vertically-deployed echosounders and recommendations for their use are pointed out. Although no scattering layers were detected in the bathypelagic zone, this study unveils an area never before recorded acoustically.

Image reconstruction algorithm for laser-induced ultrasonic imaging: The single sensor scanning synthetic aperture focusing technique.

This paper aims to implement a laser-induced ultrasound imaging reconstruction method based on the delay-and-sum beamforming through the synthetic aperture focusing technique (SAFT) for a circular scanning, performed with a tomograph that had one acoustic sensor and a system that rotates the sample around a fixed axis. The proposed method, called the Single-sensor Scanning Synthetic Aperture Focusing Technique, considers the size of the sensor and the detection procedure inside the SAFT's algebra. This image reconstruction method was evaluated numerically, using the Green function for the laser-induced ultrasound wave equation to generate a forward problem, and experimentally, using a solid object of polylactic acid, and a Sprague-Dawley rat heart located in a tissue-mimicking phantom. The resulting images were compared to those obtained from the time reversal and the conventional delay-and-sum reconstruction algorithms. The presented method removes the sidelobe artifacts and the comet tail sign, which produces a more distinguishable target on the image. In addition, the proposed method has a faster performance and lower computational load. The implementation of this method in photoacoustic microscopy techniques for image reconstruction is discussed.

Synthetic radial aperture focusing to regulate manual volumetric scanning for economic transrectal ultrasound imaging

In this paper, we present a volumetric transrectal ultrasound (TRUS) imaging under the presence of radial scanning angle disorientation (SAD) in a resource-limited diagnostic setting. Herein, we test our hypothesis that a synthetic radial aperture focusing (TRUS-rSAF) technique, in which a radial plane in target volume is reconstructed by coherent compounding of multiple transmittance/reception events, will reject a randomized SAD in a free-hand scanning setup based on external angular tracking. Based on an analytical model of the TRUS-rSAF technique, we first tested specific scenarios using a clinically available TRUS transducer under different SADs in a range of normal distributions (σ = 0.1°, 0.2°, 0.5°, 1°, 2°, and 5°). We found a benefit of the TRUS-rSAF technique for higher robustness when the SAD is contained within the radial synthetic aperture window, i.e., ±0.71° from a target scanning angle. However, no enhancement was found in spatial resolution because of the limited transmit beam field of the clinical TRUS transducer, limiting the synthetic aperture window. We further evaluated the TRUS-rSAF technique with a modified TRUS transducer for an extended synthetic aperture window to test whether higher spatial resolution and robustness to SAD can be obtained in the same evaluation setup. Widening of the synthetic aperture window (±3.54°, ± 5.91°, ± 8.27°, ± 10.63°, ± 12.99°, ± 15.35°) resulted in proportional enhancements of spatial resolution, but it also progressively built up sidelobe artifacts due to randomized synthesis with limited phase cancellations. The results suggest the need for careful calibration of the TRUS-rSAF technique to enable TRUS imaging with free-hand radial scanning and external angle tracking in resource-limited settings.

Application of low-complexity generalized coherence factor to in vivo data.

Beamforming using the generalized coherence factor (GCF) reduces sidelobe artifacts and provides an excellent contrast-to-noise ratio. We previously proposed GCFreal, a method to calculate GCF without generating analytic signals, and GCFbin, a method to calculate GCF by binarizing the received signals. In this study, we applied these methods to in vivo data and showed the effect of the computational complexity reduction on contrast performance. Channel RF data were acquired from the human liver and gallbladder. We set up several observation points in each data set and investigated the mechanism that causes the differences in contrast performance among the methods based on the signals and their power spectra in the channel direction. For GCF and GCFreal, the obtained values were almost the same. However, there were large differences in GCFbin from GCF when the signals from the focus point or from outside the focus point were received on different channels. This is because the amplitudes of the signals with high coherence and those with low coherence were changed by binarizing the signals. While GCFbin can significantly reduce the computational complexity, there are differences in the values of GCFbin and GCF due to binarizing of the received signals. However, this difference resulted in GCFbin being superior to GCF in terms of artifact reduction. This is owing to the elimination of amplitude information in GCFbin, which makes it a new efficient coherence factor with different characteristics from GCF.

A Novel Approach for the Detection of Every Significant Collapsing Bubble in Passive Cavitation Imaging.

Passive cavitation image (PCI) shows the power distribution of the acoustic emissions resulting from cavitation bubble collapses. The conventional PCI convolves the emitted cavitation signals with the point spread function of an imaging system, and it suffers from a low spatial resolution and contrast due to the increased sidelobe artifacts accumulated during the temporal integral process. To overcome the problems, the present study considers a 3-D time history of instantaneous PCIs where cavitation occurs at the local maxima of the main lobes of the beamformed cavitation field surrounded by the sidelobes largely spreading out in a time-space domain. A spatial and temporal gating technique was employed to detect the local maxima so that cavitation bubbles can be identified with their collapsing strength. The proposed approach was verified by the simulation for single and multiple cavitation bubbles, proving that it accurately detects the location and strength of the collapsing bubbles. An experimental test was also carried out for the cavitation bubbles produced by a clinical extracorporeal shock wave therapeutic device, which underpins that the proposed method successfully identifies every individual cavitation bubble.

Convolutional dictionary learning for blind deconvolution of optical coherence tomography images.

In this study, we demonstrate a sparsity-regularized, complex, blind deconvolution method for removing sidelobe artefacts and stochastic noise from optical coherence tomography (OCT) images. Our method estimates the complex scattering amplitude of tissue on a line-by-line basis by estimating and deconvolving the complex, one-dimensional axial point spread function (PSF) from measured OCT A-line data. We also present a strategy for employing a sparsity weighting mask to mitigate the loss of speckle brightness within tissue-containing regions caused by the sparse deconvolution. Qualitative and quantitative analyses show that this approach suppresses sidelobe artefacts and background noise better than traditional spectral reshaping techniques, with negligible loss of tissue structure. The technique is particularly useful for emerging OCT applications where OCT images contain strong specular reflections at air-tissue boundaries that create large sidelobe artefacts.

Investigation Study of Ultrasound Practitioners' Awareness about Artefacts of Hepatobiliary Imaging in Almadinah Almunawwarah.

Objectives:To investigate the knowledge and awareness of ultrasound practitioners’ concerning ultrasound artefacts in evaluating the hepatobiliary system.Methods:This electronic questionnaire-based comparative study involved the ultrasound practitioners’ who work in the radiology departments in Almadinah Almunawwarah governmental hospitals during the period from 1 November 2020 to 30 April 2021. Spearman’s rho correlation test was used to correlate between knowledge and job, academic qualification, and years of experience. A T-test and cross tabulation test were done to compare the knowledge about artefacts among radiologists and radiologic technologists.Results:This study involved 94 participants distributed as 22 (23.4%) radiologists and 72 (76.6%) radiologic technologists. The results shows that 85%, 71%, 73%, 69%, 54% and 53% of the participants assigned the acoustic shadowing, acoustic enhancement, ring down, side lobe, reverberation and mirror artefacts, as artefacts respectively. However, 68%, 53%, 19%, 19%, 18%, and 40% of the participants gave correct final diagnosis of acoustic shadowing, acoustic enhancement, ring down, side lobes, reverberation, and mirror artifacts, respectively. Spearman’s rho correlation test shows significant correlation between participants with more than three years experience and knowledge related mirror artefacts (r=0.328, p=0.001). It shows significant correlation between radiologists with knowledge related mirror artefacts (r=0.367, p<0.001). A significant correlation was found between highly qualified participants and knowledge related mirror artefacts (r=0.336, p=0.001) and side lobe artefacts (r=0.237, p=0.008).Conclusion:The questionnaire-based comparative study of knowledge about artefacts of hepatobiliary ultrasound imaging reveals a high level of Ultrasound practitioners’ knowledge in differentiating artefacts from pathology with a high level of knowledge in identifying hepatobiliary acoustic shadowing and acoustic enhancement artefacts. However, insufficient knowledge was noted in identifying mirror, side lobe, reverberation and ring down artefacts. A direct link was found between academic qualification, years of experience and practioners’ knowledge among.

Phase-weighted slant stacking for surface wave dispersion measurement

SUMMARY Surface wave retrieval from ambient noise records using seismic interferometry techniques has been widely used for multiscale shear wave velocity (Vs) imaging. One key step during Vs imaging is the generation of dispersion spectra and the extraction of a reliable dispersion curve from the retrieved surface waves. However, the sparse array geometry usually affects the ability for high-frequency (&amp;gt;1 Hz) seismic signals’ acquisition. Dispersion measurements are degraded by array response due to sparse sampling and often present smeared dispersion spectra with sidelobe artefacts. Previous studies usually focus on interferograms’ domain (e.g. cross-correlation function) and attempt to enhance coherent signals before dispersion measurement. We propose an alternative technique to explicitly deblur dispersion spectra through use of a phase-weighted slant-stacking algorithm. Numerical examples demonstrate the strength of the proposed technique to attenuate array responses as well as incoherent noise. Three different field examples prove the flexibility and superiority of the proposed technique: the first data set consists of ambient noise records acquired using a nodal seismometer array; the second data set utilizes distributed acoustic sensing (DAS) and a marine fibre-optic cable to acquire a similar ambient noise data set; the last data set is a vibrator-based active-source surface wave data. The enhanced dispersion measurements provide cleaner and higher-resolution spectra without distortions which will assist both human interpreters as well as ML algorithms in efficiently picking curves for subsequent Vs inversion.

Noise Adaptive Beamforming for Linear Array Photoacoustic Imaging

The delay-and-sum (DAS) algorithm is widely used for beamforming in linear array photoacoustic (PA) imaging systems and is characterized by fast execution. However, these algorithms suffer from various drawbacks, such as low resolution, low contrast, high sidelobe artifacts, and lack of visual coherence. More recently, adaptive weighting was introduced to improve the reconstruction image quality. Unfortunately, the existing state-of-the-art adaptive beamforming algorithms are computationally expensive and do not consider the specific noise characteristics of the acquired ultrasonic signal. In this article, we present a new adaptive weighting factor named the variational coherence factor (VCF), which takes into account the noise level variations of radio frequency data. The proposed technique provides superior results in terms of image resolution, sidelobe reduction, signal-to-noise ratio (SNR), and contrast level improvement. The quantitative results of the microsphere phantom imaging show that the proposed VCF-assisted DAS method leads to 55% and 25% improvements in full-width-at-half-maximum (FWHM) and 57% and 32% improvement in SNR, respectively, compared to the state-of-the-art DAS-based methods. The results demonstrate that the proposed method can effectively improve the reconstructed image quality and deliver satisfactory imaging performance even with a limited number of sensor elements. The proposed method can potentially reduce the instrumentation cost of the PA imaging system and contribute toward the clinical translation of the modality.

Iterative Model-Based Beamforming for High Dynamic Range Applications.

Clutter produced using bright acoustic sources can obscure weaker acoustic targets, degrading the quality of the image in scenarios with high dynamic ranges. Many adaptive beamformers seek to improve image quality by reducing these sidelobe artifacts, generating a boost in contrast ratio or contrast-to-noise ratio. However, some of these beamformers inadvertently introduce a dark region artifact in place of the strong clutter, a situation that occurs when both clutter and the underlying signal of interest are removed. We introduce the iterative aperture domain model image reconstruction (iADMIRE) method that is designed to reduce clutter while preserving the underlying signal. We compare the contrast ratio dynamic range (CRDR) of iADMIRE to several other adaptive beamformers plus delay-and-sum (DAS) to quantify the accuracy and reliability of the reported measured contrast for each beamformer over a wide range of contrast levels. We also compare all beamformers in the presence of bright targets ranging from 40 to 120 dB to observe the presence of sidelobes. In cases with no added reverberation clutter, iADMIRE had a CRDR of 75.6 dB when compared with the next best method DAS with 60.8 dB. iADMIRE also demonstrated the best performance for levels of reverberation clutter up to 0-dB signal-to-clutter ratio. Finally, iADMIRE restored underlying speckle signal in dark artifact regions while suppressing sidelobes in bright target cases up to 100 dB.

Simultaneous multi-slice T1 mapping using MOLLI with blipped CAIPIRINHA bSSFP

BackgroundThis study evaluates the possibility for replacing conventional 3 slices, 3 breath-holds MOLLI cardiac T1 mapping with single breath-hold 3 simultaneous multi-slice (SMS3) T1 mapping using blipped-CAIPIRINHA SMS-bSSFP MOLLI sequence. As a major drawback, SMS-bSSFP presents unique artefacts arising from side-lobe slice excitations that are explained by imperfect RF modulation rendering and bSSFP low flip angle enhancement. Amplitude-only RF modulation (AM) is proposed to reduce these artefacts in SMS-MOLLI compared to conventional Wong multi-band RF modulation (WM). Materials and methodsPhantoms and ten healthy volunteers were imaged at 1.5 T using a modified blipped-CAIPIRINHA SMS-bSSFP MOLLI sequence with 3 simultaneous slices. WM-SMS3 and AM-SMS3 were compared to conventional single-slice (SMS1) MOLLI. First, SNR degradation and T1 accuracy were measured in phantoms. Second, artefacts from side-lobe excitations were evaluated in a phantom designed to reproduce fat presence near the heart. Third, the occurrence of these artefacts was observed in volunteers, and their impact on T1 quantification was compared between WM-SMS3 and AM-SMS3 with conventional MOLLI as a reference. ResultsIn the phantom, larger slice gaps and slice thicknesses yielded higher SNR. There was no significant difference of T1 values between conventional MOLLI and SMS3-MOLLI (both WM and AM). Positive banding artefacts were identified from fat neighbouring the targeted FOV due to side-lobe excitations from WM and the unique bSSFP signal profile. AM RF pulses reduced these artefacts by 38%.In healthy volunteers, AM-SMS3-MOLLI showed similar artefact reduction compared to WM-SMS3-MOLLI (3 ± 2 vs 5 ± 3 corrupted LV segments out of 16). In-vivo native T1 values obtained from conventional MOLLI and AM-SMS3-MOLLI were equivalent in LV myocardium (SMS1-T1 = 935.5 ± 36.1 ms; AM-SMS3-T1 = 933.8 ± 50.2 ms; P = 0.436) and LV blood pool (SMS1-T1 = 1475.4 ± 35.9 ms; AM-SMS3-T1 = 1452.5 ± 70.3 ms; P = 0.515). Identically, no differences were found between SMS1 and SMS3 postcontrast T1 values in the myocardium (SMS1-T1 = 556.0 ± 19.7 ms; SMS3-T1 = 521.3 ± 28.1 ms; P = 0.626) and the blood (SMS1-T1 = 478 ± 65.1 ms; AM-SMS3-T1 = 447.8 ± 81.5; P = 0.085). ConclusionsCompared to WM RF modulation, AM SMS-bSSFP MOLLI was able to reduce side-lobe artefacts considerably, providing promising results to image the three levels of the heart in a single breath hold. However, few artefacts remained even using AM-SMS-bSSFP due to residual RF imperfections. The proposed blipped-CAIPIRINHA MOLLI T1 mapping sequence provides accurate in vivo T1 quantification in line with those obtained with a single slice acquisition.

Source Density Apodization: Image Artifact Suppression Through Source Pitch Nonuniformity.

Conventional ultrasound imaging probes typically comprise finite-sized arrays of periodically spaced transducer elements which, in the case of phased arrays, can result in severe grating and sidelobe artifacts. Whereas side lobes can be effectively suppressed through amplitude apodization ("AmpA"), grating lobes arising from periodicity in transducer placement can only be suppressed by decreasing the element pitch, which is technologically challenging and costly. In this work, we present source density apodization ("SDA") as an alternative apodization scheme, where the spatial source density (and, hence, the element pitch) is varied across the imaging aperture. Using an all-optical ultrasound imaging setup capable of video-rate 2-D imaging as well as dynamic and arbitrary reconfiguration of the source array geometry, we show both numerically and experimentally how SDA and AmpA are equivalent for large numbers of sources. For low numbers of sources, SDA is shown to yield superior image quality as both side and grating lobes are effectively suppressed. In addition, we demonstrate how asymmetric SDA schemes can be used to locally and dynamically improve the image quality. Finally, we demonstrate how a nonsmoothly varying spatial source density (such as that obtained for randomized arrays or in the presence of source positioning uncertainty or inaccuracy) can yield severe image artifacts. The application of SDA can, thus, yield high image quality even for low channel counts, which can ultimately result in higher imaging frame rates using acquisition systems of reduced complexity.

Applications of QC and Merged Doppler Spectral Density Data from Ka-Band Cloud Radar to Microphysics Retrieval and Comparison with Airplane in Situ Observation

The new Chinese Ka-band solid-state transmitter cloud radar (CR) uses four operational modes with different pulse widths and coherent integration and non-coherent integration numbers to meet long-term cloud measurement requirements. The CR and an instrument-equipped aircraft were used to observe clouds and precipitation on the east side of Taihang Mountain in Hebei Province in 2018. To resolve the data quality problems caused by attenuation in the precipitation area; we focused on developing an algorithm for attenuation correction based on rain drop size distribution (DSD) retrieved from the merged Doppler spectral density data of the four operational modes following data quality control (QC). After dealiasing Doppler velocity and removal of range sidelobe artifacts; we merged the four types of Doppler spectral density data. Vertical air speed and DSD are retrieved from the merged Doppler spectral density data. Finally, we conducted attenuation correction of Doppler spectral density data and recalculated Doppler moments such as reflectivity; radial velocity; and spectral width. We evaluated the consistencies of reflectivity spectra from the four operational modes and DSD retrieval performance using airborne in situ observation. We drew three conclusions: First, the four operational modes observed similar reflectivity and velocity for clouds and low-velocity solid hydrometeors; however; three times of coherent integration underestimated Doppler reflectivity spectra for velocities greater than 2 m s−1. Reflectivity spectra were also underestimated for low signal-to-noise ratios in the low-sensitivity operational mode. Second, QC successfully dealiased Doppler velocity and removed range sidelobe artifacts; and merging of the reflectivity spectra mitigated the effects of coherent integration and pulse compression on radar data. Lastly, the CR observed similar DSD and liquid water content vertical profiles to airborne in situ observations. Comparing CR and aircraft data yielded uncertainty due to differences in observation space and temporal and spatial resolutions of the data.

Optical Coherence Tomography With Gapped Spectrum

The axial resolution of optical coherence tomography (OCT) is determined by the spectral shape and bandwidth of the detected light, which are limited by the gaps in the wavelength range of illumination, transmission, and detection. In this paper, we demonstrate that the axial resolution deteriorated by gaps in OCT spectra can be restored by adopting the gapped amplitude and phase estimation (GAPES) method. GAPES estimates the missing parts between separated spectral bands and obtains the axial profile of tissue with reduced sidelobe artifacts compared to the gapped spectra and significantly improved axial resolution over the individual bands. This technique may make it possible to combine spectrally separated sources and detectors to improve axial resolution in OCT images.

Joint SAR Imaging and Multi-Feature Decomposition From 2-D Under-Sampled Data Via Low-Rankness Plus Sparsity Priors

In this paper, we introduce a multi-feature decomposition approach to the problem of synthetic aperture radar (SAR) image reconstruction from under-sampled data in both range and azimuth directions. Conventional SAR image formation methods may produce images that are not appropriate for interpretation tasks such as segmentation and automatic target recognition. We deal with this problem using an efficient joint SAR image reconstruction–decomposition framework in which features of interest are enhanced and decomposed simultaneously. Unlike conventional methods, our proposed framework provides multiple segment images along with a composite SAR image. In the composite image not only the resolution is improved but also both the speckle and sidelobe artifacts are reduced. In the decomposed images, different components can be roughly attributed to different potential segments, which facilitate the subsequent interpretation tasks such as shape-based recognition or region segmentation. Moreover, these decomposed images contain easier-to-segment regions rather than taking the entire scene for segmenting the feature of interest. By formulating the SAR image reconstruction as a low-rank plus multi-feature decomposition problem, the optimization problem is solved based on the alternating direction method of multipliers. Using combined dictionaries, multiple transform-sparse components are represented efficiently by a linear combination of multiple sparsifying matrices associated with the features of interest in the scene. Our proposed method jointly reconstructs and decomposes different pieces of the imaged SAR scene, in particular the low-rank part of the background and sparsely represented features of interest, from under-sampled observed data. Using extensive experimental results we show the effectiveness of the proposed method on both synthetic and real SAR images.

A method of sidelobe effect suppression for multibeam water column images based on an adaptive soft threshold

Recording abundant back scatter information in the water, multibeam sonar water column imaging (WCI) has become one of the most effective means for the acoustic remote sensing investigation of water targets and has been applied and developed rapidly for sounding, fishery, oceanography and archaeology applications as well as in other industries in recent years. Due to the signal interference caused by the sidelobes, there is a great deal of noise in non-target areas of WCI, which reduces the accuracy and efficiency of interpretation of WCI and hinders the automatic detection and recognition of underwater objects. In this paper, an adaptive soft threshold denoising algorithm for WCI is proposed. The WCI data are divided into background areas and target-noise mixing areas by analysing the mathematical features of all angle sequences; thus, the target, noise, and sidelobe artefacts are separated using adjustable threshold parameters and by suppressing noise and sidelobe artefacts to obtain a clearer image. Lastly, the measured data are used for verification. The results indicate that the algorithm has a certain reference value for sidelobe suppression of multibeam WCI.