Edge Artifact Research Articles

The edge artifact extraction method for Si3N4 ceramic bearing rolling element microcracks characterization

Aiming at the problem that the edge artifacts of Si3N4 ceramic bearing rolling element microcracks have low contrast, contain noise, and easily merge with the background, making it difficult to segment. A method based on 2D discrete wavelet transform and Otsu threshold segmentation is designed to achieve the extraction of microcrack edge artifact features. Wavelet decomposition is used to remove noise, while wavelet reconstruction features are used to restore lost details. Creation of 2D discrete wavelet transform functional equations combining wavelet reconstruction and wavelet decomposition to improve contrast and eliminate noise in images featuring edge artifacts. For the problem of feature edge artifacts that are difficult to remove, the threshold segmentation function equation is designed to maximize the interclass variance, and the optimal threshold value is selected to remove the edge artifacts. The experimental results show that the average PSNR of the Si3N4 ceramic bearing rolling body point, line, and surface microcrack edge artifact feature images enhanced by the method in this paper is close to 62.69 dB, and the average SSIM is about 0.77. The method in this paper improves the contrast of microcrack edge artifact features of Si3N4 ceramic bearing rolling bodies and makes the feature extraction effect of point, line, and surface microcrack edge artifacts more complete.

Impact of list-mode reconstruction and image-space point spread function correction on PET image contrast and quantitative value using SiPM-based PET/CT system.

We evaluate the effects of list-mode reconstruction and the image-space point spread function (iPSF) on the contrast and quantitative values of positron emission tomography (PET) images using a SiPM-PET/CT system. The evaluation is conducted on an NEMA body phantom and clinical images using a Cartesion Prime SiPM-PET/CT system. The signal-to-background ratio (SBR) of the phantom is set to 2, 4, 6, and 8, and all the PET image data are obtained and reconstructed using 3D-OSEM, time-of-flight, iPSF (-/ +), and a 4-mm Gaussian filter with several iterations. The evaluation criteria include % background variability (NB,10mm), % contrast (QH,10mm), iPSF change in QH,10mm (ΔQH,10mm) for edge artifact evaluation, profile curves, visual evaluation of edge artifacts, clinical imaging for the standardized uptake value (SUV) of lung nodules, and SNRliver. NB,10mm demonstrates no significant difference in all SBRs with and without iPSF, whereas QH,10mm is higher based on the SBR with and without iPSF. ΔQH,10mm indicates increased iterations and a larger rate of change (> 5%) for small spheres of < 17mm. The profile curves portrayed almost real concentrations, except for the 10-mm sphere of SBR2 without iPSF; however, with iPSF, an overshoot was observed in the 13-mm sphere of all SBRs. The degree of overshoot increased with increasing iteration and SBR. Edge artifacts were detected at values ≥ 17-22mm in SBRs other than SBR2 with iPSF. Irrespective of the nodal size, SUV and SNRliver improved considerably after iPSF adjustment. Therefore, the effects of list-mode reconstruction and iPSF on PET image contrast were limited, and the overcorrection of the quantitative values was validated using iPSF.

Nanoscale chemical and structural investigation of solid solution polyelemental transition metal oxide nanoparticles

Although it has been shown that configurational entropy can improve the structural stability in transition metal oxides (TMOs), little is known about the oxidation state of transition metals under random mixing of alloys. Such information is essential in understanding the chemical reactivity and properties of TMOs stabilized by configurational entropy. Herein, utilizing electron energy loss spectroscopy (EELS) technique in an aberration-corrected scanning transmission electron microscope (STEM), we systematically studied the oxidation state of binary (Mn,Fe)3O4, ternary (Mn, Fe, Ni)3O4, and quinary (Mn, Fe, Ni, Cu, Zn)3O4 solid solution polyelemental transition metal oxides (SSP-TMOs) nanoparticles. Our findings show that the random mixing of multiple elements in the form of solid solution phase not only promotes the entropy stabilization but also results in stable oxidation state in transition metals spanning from binary to quinary transition metal oxide nanoparticles.

The Impact of Black Edge Artifact on QoE of the FOV-Based Cloud VR Services

The Impact of Black Edge Artifact on QoE of the FOV-Based Cloud VR Services

Image manipulation detection by multiple tampering traces and edge artifact enhancement

Image manipulation detection has attracted considerable attention owing to the increasing security risks posed by fake images. Previous studies have proven that tampering traces hidden in images are essential for detecting manipulated regions. However, existing methods have limitations in generalization and the ability to tackle post-processing methods. This paper presents a novel Network to learn and Enhance Multiple tampering Traces (EMT-Net), including noise distribution and visual artifacts. For better generalization, EMT-Net extracts global and local noise features from noise maps using transformers and captures local visual artifacts from original RGB images using convolutional neural networks. Moreover, we enhance fused tampering traces using the proposed edge artifacts enhancement modules and edge supervision strategy to discover subtle edge artifacts hidden in images. Thus, EMT-Net can prevent the risks of losing slight visual clues against well-designed post-processing methods. Experimental results indicate that the proposed method can detect manipulated regions and outperform state-of-the-art approaches under comprehensive quantitative metrics and visual qualities. In addition, EMT-Net shows robustness when various post-processing methods further manipulate images.

Potential Pitfalls of Steatopygia on Bone Scintigraphy and the Added Value of Single Photon Emission Computed Tomography and Single Photon Emission Computed Tomography/Computed Tomography

Objectives: The aims of the study were to assess the prevalence of steatopygia on bone scintigraphy of obese patients and to evaluate its effect on the appearance of the lumbar spine, and the added benefit of single photon emission computed tomography (SPECT) and SPECT/computed tomography (SPECT/CT) in overcoming possible artifacts. Methods: Patients with BMI ≥30 who underwent bone scintigraphy between 2016 and 2019 were included. Three nuclear medicine consultants reviewed the studies to determine whether significant steatopygia was present, and whether it resulted in attenuation of underlying lumber spine and crease edge artifact. SPECT or SPECT/CT images were reviewed to evaluate their impact on diagnosis. Results: 56 out of the 100 patients were noted to have steatopygia on planar images. Among the group of 80 obese patients, 50% showed steatopygia, while in the group of 20 morbidly obese patients, 80% showed steatopygia. 32 of the 56 patients with steatopygia had significant attenuation at the lower lumber vertebrae. Nine of these patients showed crease edge artifact. SPECT and SPECT/CT clarified the scintigraphic abnormalities noted in all patients including those with edge artifact alleviating diagnostic difficulty. Among the 9 patients with edge artifact, 6 patients showed normal appearance on SPECT/CT images while three showed true abnormalities. Conclusions: Steatopygia is common on bone scintigraphy of obese patients, higher in females and morbidly obese patients. Obesity-related artifacts in bone scintigraphy, including attenuation effect and edge artifact, are common in this patient group. SPECT or SPECT/CT improves the diagnostic accuracy by overcoming the effects of steatopygia seen on planar images.

Automatic Electrophysiological Noise Reduction and Epileptic Seizure Detection for Stereoelectroencephalography.

The objective of this study was to develop a computational algorithm capable of locating artifacts and identifying epileptic seizures, which specifically implementing in clinical stereoelectroencephalography (SEEG) recordings. Based on the nonstationary nature and broadband features of SEEG signals, a comprehensive strategy combined with the complex wavelet transform (CWT) and multi-layer thresholding method was implemented for both noise reduction and seizure detection. The artifacts removal pipeline integrated edge artifact removal, discrete spectrum analysis, and peak density evaluation. For automatic seizure detection, integrated power analysis and multi-dynamic thresholding were applied. The F1score was applied to evaluate overall performance of the algorithm. The algorithm was tested using expert-marked, double-blinded, clinical SEEG data from seven patients undergoing presurgical evaluation. This approach achieved the F1 score of 0.86 for noise reduction and 0.88 for seizure detection. This offline-approach method with minimum parameter tuning procedures and no prior information required, proved to be a feasible and solid solution for clinical SEEG data evaluation. Moreover, the algorithm can be improved with additional tuning and implemented with machine learning postprocessing pipelines.

Comparison of Complex k-Space Data and Magnitude-Only for Training of Deep Learning–Based Artifact Suppression for Real-Time Cine MRI

Propose: The purpose of this study was to compare the performance of deep learning networks trained with complex-valued and magnitude images in suppressing the aliasing artifact for highly accelerated real-time cine MRI.Methods: Two 3D U-net models (Complex-Valued-Net and Magnitude-Net) were implemented to suppress aliasing artifacts in real-time cine images. ECG-segmented cine images (n = 503) generated from both complex k-space data and magnitude-only DICOM were used to synthetize radial real-time cine MRI. Complex-Valued-Net and Magnitude-Net were trained with fully sampled and synthetized radial real-time cine pairs generated from highly undersampled (12-fold) complex k-space and DICOM images, respectively. Real-time cine was prospectively acquired in 29 patients with 12-fold accelerated free-breathing tiny golden-angle radial sequence and reconstructed with both Complex-Valued-Net and Magnitude-Net. Cardiac function, left-ventricular (LV) structure, and subjective image quality [1(non-diagnostic)-5(excellent)] were calculated from Complex-Valued-Net– and Magnitude-Net–reconstructed real-time cine datasets and compared to those of ECG-segmented cine (reference).Results: Free-breathing real-time cine reconstructed by both networks had high correlation (all R2 &amp;gt; 0.7) and good agreement (all p &amp;gt; 0.05) with standard clinical ECG-segmented cine with respect to LV function and structural parameters. Real-time cine reconstructed by Complex-Valued-Net had superior image quality compared to images from Magnitude-Net in terms of myocardial edge sharpness (Complex-Valued-Net = 3.5 ± 0.5; Magnitude-Net = 2.6 ± 0.5), temporal fidelity (Complex-Valued-Net = 3.1 ± 0.4; Magnitude-Net = 2.1 ± 0.4), and artifact suppression (Complex-Valued-Net = 3.1 ± 0.5; Magnitude-Net = 2.0 ± 0.0), which were all inferior to those of ECG-segmented cine (4.1 ± 1.4, 3.9 ± 1.0, and 4.0 ± 1.1).Conclusion: Compared to Magnitude-Net, Complex-Valued-Net produced improved subjective image quality for reconstructed real-time cine images and did not show any difference in quantitative measures of LV function and structure.

Ultrasound segmentation-guided edge artifact reduction in diffuse optical tomography using connected component analysis.

Ultrasound (US)-guided diffuse optical tomography (DOT) has demonstrated potential value for breast cancer diagnosis and treatment response assessment. However, in clinical use, the chest wall, poor probe-tissue contact, and tissue heterogeneity can all cause image artifacts. These image artifacts, appearing commonly as hot spots in the non-lesion regions (edge artifacts), can decrease the reconstruction accuracy and cause misinterpretation of lesion images. Here we introduce an iterative, connected component analysis-based image artifact reduction algorithm. A convolutional neural network (CNN) is used to segment co-registered US images to extract the lesion location and size to guide the artifact reduction. We demonstrate its performance using Monte Carlo simulations on VICTRE digital breast phantoms and breast patient images. In simulated tissue mismatch models, this algorithm successfully reduces edge artifacts without significantly changing the reconstructed target absorption coefficients. With clinical data it improves the optical contrast between malignant and benign groups, from 1.55 without artifact reduction to 1.91 with artifact reduction. The proposed algorithm has a broad range of applications in other modality-guided DOT imaging.

Use of Deep Learning for Weather Radar Nowcasting

AbstractNowcasting based on weather radar uses the current and past observations to make estimations of future radar echoes. There are many types of operationally deployed nowcasting systems, but none of them are currently based on deep learning, despite it being an active area of research in the last few years. This paper explores deep learning models as alternatives to current methods by proposing different architectures and comparing them against some operational nowcasting systems. The methods proposed here, harnessing residual convolutional encoder-decoder architectures, reach a level of performance expected of current systems and in certain scenarios can even outperform them. Finally, some of the potential drawbacks of using deep learning are analyzed. No decay in the performance on a different geographical area from where the models were trained was found. No edge or checkerboard artifact, common in convolutional operations, was found that affects the nowcasting metrics.

Origin and Suppression of Beam Damage-Induced Oxygen-K Edge Artifact from γ-Al2O3 using Cryo-EELS

Gamma-alumina (γ-Al2O3), like other low-Z oxides, is readily damaged when exposed to an electron beam. This typically results in the formation of a characteristic pre-edge peak in the oxygen-K edge of electron energy-loss spectra (EELS) acquired during or after the damage process. This artifact can mask the presence of intrinsic O-K edge fine structure that would reveal chemical properties of the material; therefore, its suppression is key. In this work, we systematically investigate the conditions that give rise to the damage-induced O-K pre-edge peak and show that it can be effectively suppressed by performing EELS experiments at cryogenic (cryo) temperatures. Prolonged exposure of γ-Al2O3 to a focused electron beam results in a hole bored through the sample; this was used as a reproducible beam damage condition. O-K edge EELS spectra were collected from a single-crystal γ-Al2O3 sample both during and after focused electron beam hole drilling, and at room and cryo temperatures, using a monochromated scanning transmission electron microscope (STEM). The characteristic 531 eV pre-edge peak visible in the room temperature EELS spectra was completely suppressed in the cryo-EELS spectra, even in the presence of a visible drilled hole. We then correlated these experimental observations with multiple-scattering EELS simulations to determine the likely atomistic origin of the damage-induced O-K pre-edge peak. The findings indicate that the pre-edge peak is caused primarily by the presence of surface O-O bonds formed during beam damage, and that operating at cryo temperature suppresses the formation of surface O-O bonds, thus preventing formation of the O-K pre-edge peak. Additionally, Al-L2,3 edge EELS spectra revealed Al loss primarily from tetrahedral sites during hole drilling.

High-Precision Cutting Edge Radius Measurement of Single Point Diamond Tools Using an Atomic Force Microscope and a Reverse Cutting Edge Artifact

This paper presents a measurement method for high-precision cutting edge radius of single point diamond tools using an atomic force microscope (AFM) and a reverse cutting edge artifact based on the edge reversal method. Reverse cutting edge artifact is fabricated by indenting a diamond tool into a soft metal workpiece with the bisector of the included angle between the tool’s rake face and clearance face perpendicular to the workpiece surface on a newly designed nanoindentation system. An AFM is applied to measure the topographies of the actual and the reverse diamond tool cutting edges. With the proposed edge reversal method, a cutting edge radius can be accurately evaluated based on two AFM topographies, from which the convolution effect of the AFM tip can be reduced. The accuracy of the measurement of cutting edge radius is significantly influenced by the geometric accuracy of reverse cutting edge artifact in the proposed measurement method. In the nanoindentation system, the system operation is optimized for achieving high-precision control of the indentation depth of reverse cutting edFigurege artifact. The influence of elastic recovery and the AFM cantilever tip radius on the accuracy of cutting edge radius measurement are investigated. Diamond tools with different nose radii are also measured. The reliability and capability of the proposed measurement method for cutting edge radius and the designed nanoindentation system are demonstrated through a series of experiments.

Detection of sub-centimeter lesions using digital TOF-PET/CT system combined with Bayesian penalized likelihood reconstruction algorithm.

Many advances in PET/CT technology can potentially improve image quality and the ability to detect small lesions. A new digital TOF-PET/CT scanner based on silicon photomultipliers (SiPM) integrated with a Bayesian penalized likelihood (BPL) PET reconstruction algorithm (Q.Clear; GE Healthcare) has been introduced into clinical practice. The present study aimed to quantify the ability of a digital TOF-PET/CT scanner combined with BPL reconstruction to detect small lesions, and to determine the optimal penalization factor (β) in BPL to accurately detect such lesions. All PET data were acquired from a NEMA body phantom using a Discovery MI (DMI) PET/CT system (GE Healthcare). The phantom included six spheres with diameters of 4, 5, 6, 8, 10, and 13mm, and contained a background activity level of 5.3kBq/mL, with target-to-background ratios (TBR) of 4:1 and 8:1. Images were reconstructed using a baseline OSEM algorithm, with OSEM + PSF, OSEM + TOF, OSEM + PSF + TOF, and BPL + PSF + TOF (β: 50-400). The matrix size was 192 × 192 and 384 × 384. Data acquired in 100-min list mode were re-binned into acquisition times ranging from 2 to 100min. The quantitative accuracy and detectability of small hot spheres were evaluated by physical assessment of a recovery coefficient (RC) and a detectability index (DI), as well as visual assessment of PET images at each acquisition time. The RC and DI of sub-centimeter spheres were improved, because the digital TOF-PET/CT scanner has a larger TOF performance gain due to better timing resolution. The RC and DI were higher with BPL in sub-centimeter spheres, than with other OSEM-based types of reconstruction. The BPL for an 8-mm sphere overestimated uptake due to edge artifact overshoot induced by PSF modeling. The variability of RC and DI for acquisition times and TBR differed considerably according to β values. The RC for ~ 8-mm spheres were > 1 at β values between 50 and 100, but were close to 1 at β value of 200. The visual scores for β = 200 in BPL were maximal, whereas those for spheres that were ≥ 6mm exceeded the criterion of 3. The BPL in the digital TOF-PET/CT scanner improved the quantitation and detectability of sub-centimeter spheres compared with OSEM-based reconstruction. Optimization of the β value in BPL might allow the detection of lesions ≤ 6mm, although detectability depended on the TBR of lesions. A β value of 200 seemed optimal for detecting sub-centimeter lesions.

Evaluation of quantitative, efficient image reconstruction for VersaPET, a compact PET system.

Previously we developed a high-resolution positron emission tomography(PET) system-VersaPET-characterized by a block geometry with relatively large axial and transaxial interblock gaps and a compact geometry susceptible to parallax blurring effects. In this work, we report the qualitative and quantitative evaluation of a graphic processing unit (GPU)-accelerated maximum-likelihood by expectation-maximization (MLEM) image reconstruction framework for VersaPET which features accurate system geometry and projection space point-spread-function (PSF) modeling. We combined the ray-tracing module from software for tomographic image reconstruction (STIR),an open-source PET image reconstruction package, with VersaPET's exact block geometry for the geometric system matrix. Point-spread-function modeling of crystal penetration and scattering was achieved by a custom Monte-Carlo simulation for projection space blurring in all dimensions. We also parallelized the reconstruction in GPU taking advantage of the system's symmetry for PSF computation. To investigate the effects of PSF width, we generated and studied multiple kernels between one that reflects the true LYSO density in the MC simulation and another that reflects geometry only (no PSF). GATE simulations of hot and cold-sphere phantoms with spheres of different sizes, real microDerenzo phantom, and human blood vessel data were used to characterize the quantitative and qualitative performances of the reconstruction. Reconstruction with an accurate system geometry effectively improved image quality compared to STIR (version 3.0) which assumes an idealized system geometry. Reconstructions of GATE-simulated hot-sphere phantom data showed that all PSF kernels achieved superior performance in contrast recovery and bias reduction compared to using no PSF, but may introduce edge artifact and lumped background noise pattern depending on the width of PSF kernels. Cold-sphere phantom simulation results also indicated improvement in contrast recovery and quantification with PSF modeling (compared to no PSF) for 5 and 10mm cold spheres. Real microDerenzo phantom images with the PSF kernel that reflects the true LYSO density showed degraded resolving power of small sectors that could be resolved more clearly by underestimated PSF kernels, which is consistent with recent literature despite differences in scanner geometries and in approaches to system model estimation. The human vessel results resemble those of the hot-sphere phantom simulation with the PSF kernel that reflects the true LYSO density achieving the highest peak in the time activity curve (TAC) and similar lumped noise pattern. We fully evaluated a practical MLEM reconstruction framework that we developed for VersaPET in terms of qualitative and quantitative performance. Different PSF kernels may be adopted for improving the results of specific imaging tasks but the underlying reasons for the variation in optimal kernel for the real and simulation studies requires further study.

Color image multi-scale guided depth image super-resolution reconstruction

In order to obtain better super-resolution reconstruction results of depth images, this paper constructs a multi-scale color image guidance depth image super-resolution reconstruction convolutional neural network. In this paper, the multi-scale fusion method is used to realize the guidance of high resolution (HR) color image features to low resolution (LR) depth image features, which is beneficial to the restoration of image details. In the process of extracting features from LR depth images, a multiple receptive field residual block (MRFRB) is constructed to extract and fuse the features of different receptive fields, and then connect and fuse the features of each MRFRB output to obtain global fusion features. Finally, the HR depth image is obtained through sub-pixel convolution layer and global fusion features. The experimental results show that the super-resolution image obtained by this method alleviates the edge distortion and artifact problems, and has better visual effects.

Artifacts in Macular Optical Coherence Tomography.

Purpose:To identify and explain different artifacts in macular optical coherence tomography (OCT).Methods:For this comprehensive review, a PubMed and Google Scholar (January 1995–October 2018) search was conducted by the researchers, using the keywords such as OCT, artifacts, artefact, and macula.Results:We reviewed the main OCT artifacts including software break-down or misidentification of retinal layers, incomplete segmentation error, complete segmentation failure, mirror artifact (inverted artifact), cut edge artifact, degraded image scan, out-of-register artifact, off-center artifact, motion artifact, foveal duplication, segmentation shift, blink artifact, static or fixed image artifact, linear artifact, and perfluorocarbon liquid-producing artifact.Conclusions:There are various artifacts in OCT image scans. The identification of these artifacts may help in accurate interpretations of OCT images in clinical settings that can affect the diagnosis and management of different retinal disorders.

Novel microcomputed tomography method for thickness analysis of calcified cartilage

Purpose: Understanding of calcified cartilage morphology could provide new cues on osteoarthritis development. However, since calcified cartilage and subchondral bone share similar X-ray attenuation properties their separation from micro-computed tomography (micro-CT) images remains challenging. This study aimed at developing and validating a novel method for the segmentation and thickness analysis of calcified cartilage (CC) using micro-CT. Methods: To facilitate high-resolution micro-CT imaging, eight healthy knees from skeletally mature rabbits (N = 8) were dissected to six smaller osteochondral samples, i.e. lateral and medial femoral condyles, femoral groove, lateral and medial tibial plateau as well as patella (in total n = 48 samples). The samples were imaged with micro-CT device (isotropic voxel size: 3.2 μm). After imaging, the samples were decalcified and embedded in paraffin. Three histology sections (∼5 μm thickness) were stained with Goldner’s trichrome from each sample. Preprocessing for CC segmentation: The micro-CT data were reconstructed into two image stacks with: 1) standard and 2) mineral windows, respectively. The standard window (SW) image stack contained information from a wide attenuation coefficient range, while the mineral window (MW) image stack contained information from a narrow attenuation coefficient range providing a high contrast between CC and bone. Both stacks were co-registered. Trabecular bone volume-of-interest (VOItrab) was obtained from the SW stack with manual segmentation. VOItrab was subtracted from the SW stack. The output stack was inverted and refined with a shrink-wrap operation. This generated a subchondral plate VOI (VOIplate) including both subchondral bone and CC. CC segmentation: The region defined by VOIplate was selected from the MW stack and a narrow, high threshold window was applied to select only subchondral bone. Subsequently, the selected subchondral bone was subtracted from the VOIplate region to produce the CC segmentation. Finally, morphological opening operations were used for eliminating the edge artifact for more realistic estimation of CC thickness (CC.Th). CC thickness analysis: First, 2D analysis both from micro-CT and histology was conducted to validate the micro-CT-based CC.Th analysis. The CC.Th obtained from micro-CT (as an average of five slices) was compared with the CC.Th from histology sections using ImageJ (BoneJ plugin). Then, CC.Th was analyzed in 3D using a sphere-based method utilizing a distance transformation. Statistics: Normality of the CC.Th data were first ensured (Shapiro-Wilk test). The agreement between the 2D micro-CT and histology analysis was evaluated with a Bland-Altman plot. One-way ANOVA test (post-hoc test: Newman-Keuls) was used for comparing the CC.Th between different locations. The level of significance was set to 0.05. Results: CC.Th in 2D had a good agreement between histology and micro-CT (Figure 1A to 1C, bias: -8.6 μm, levels of agreement: [-71.3, 54.1] μm). In the 2D histology analysis (Figure 1D), the patella had the thickest CC, while the thinnest locations were found for the tibial plateau and medial femoral condyle. Interestingly, in all 2D and 3D analyses (Figure 1E and 1F) CC was always thicker in the patella compared to the femoral groove and tibial plateau. Furthermore, CC in the lateral femoral condyle was always thicker than in the medial tibial plateau. Conclusions: In this study, we presented and validated a novel method for the thickness analysis of CC from micro-CT images. In addition, we observed different CC.Th. between anatomical locations with both histology and micro-CT. The different CC.Th. between anatomical locations is most likely related with local loading conditions.In the future, this method can be used for assessing the potential changes of CC.Th. for different joint pathologies, such as osteoarthritis.

Wavelet transform based on Meyer algorithm for image edge and blocking artifact reduction

In this study, a wavelet transform based on Meyer algorithm with edge-angle tracking capability is proposed for edge and blocking artifact reduction of an image, during image compression processes. This new proposed scheme is quite different from the conventional curvelet scheme, which can be viewed as using post-processing, after wavelet transform, to track only the edge of a high-frequency section of wavelet coefficients. However, the proposed wavelet transform scheme based on Meyer algorithm is with a complete wavelet design procedure, which is suitable for multiresolution analysis; it possesses the orthonormal property of conventional wavelet transforms. From computer simulation, we verify the merits of the proposed scheme over the conventional approaches, in terms of achieving better performance based on the subjective and objective (e.g., the peak signal-to-noise ratio) analysis.

Algorithm and Architecture Optimization for 2D Discrete Fourier Transforms with Simultaneous Edge Artifact Removal

Two-dimensional discrete Fourier transform (DFT) is an extensively used and computationally intensive algorithm, with a plethora of applications. 2D images are, in general, nonperiodic but are assumed to be periodic while calculating their DFTs. This leads to cross-shaped artifacts in the frequency domain due to spectral leakage. These artifacts can have critical consequences if the DFTs are being used for further processing, specifically for biomedical applications. In this paper we present a novel FPGA-based solution to calculate 2D DFTs with simultaneous edge artifact removal for high-performance applications. Standard approaches for removing these artifacts, using apodization functions or mirroring, either involve removing critical frequencies or necessitate a surge in computation by significantly increasing the image size. We use a periodic plus smooth decomposition-based approach that was optimized to reduce DRAM access and to decrease 1D FFT invocations. 2D FFTs on FPGAs also suffer from the so-called “intermediate storage” or “memory wall” problem, which is due to limited on-chip memory, increasingly large image sizes, and strided column-wise external memory access. We propose a “tile-hopping” memory mapping scheme that significantly improves the bandwidth of the external memory for column-wise reads and can reduce the energy consumption up to 53%. We tested our proposed optimizations on a PXIe-based Xilinx Kintex 7 FPGA system communicating with a host PC, which gives us the advantage of further expanding the design for biomedical applications such as electron microscopy and tomography. We demonstrate that our proposed optimizations can lead to 2.8× reduced FPGA and DRAM energy consumption when calculating high-throughput 4096×4096 2D FFTs with simultaneous edge artifact removal. We also used our high-performance 2D FFT implementation to accelerate filtered back-projection for reconstructing tomographic data.

Edge-preserving reconstruction from sparse projections of limited-angle computed tomography using ℓ0-regularized gradient prior.

Accurate images reconstructed from limited computed tomography (CT) data are desired when reducing the X-ray radiation exposure imposed on patients. The total variation (TV), known as the l1-norm of the image gradient magnitudes, is popular in CT reconstruction from incomplete projection data. However, as the projection data collected are from a sparse-view of the limited scanning angular range, the results reconstructed by a TV-based method suffer from blocky artifact and gradual changed artifacts near the edges, which in turn make the reconstruction images degraded. Different from the TV, the ℓ0-norm of an image gradient counts the number of its non-zero coefficients of the image gradient. Since the regularization based on the ℓ0-norm of the image gradient will not penalize the large gradient magnitudes, the edge can be effectively retained. In this work, an edge-preserving image reconstruction method based on l0-regularized gradient prior was investigated for limited-angle computed tomography from sparse projections. To solve the optimization model effectively, the variable splitting and the alternating direction method (ADM) were utilized. Experiments demonstrated that the ADM-like method used for the non-convex optimization problem has better performance than other classical iterative reconstruction algorithms in terms of edge preservation and artifact reduction.