POCS Algorithm Research Articles

Prospective Interventional Cohort Study using AIIMS Simplified POC Algorithm for Restricted Blood Transfusion in Cyanotic Children

Prospective Interventional Cohort Study using AIIMS Simplified POC Algorithm for Restricted Blood Transfusion in Cyanotic Children

Adaptive optical algorithms with differentiation of water bodies based on varying composition of suspended particulate matter: A case study for estimating the particulate organic carbon concentration in the western Arctic seas

High complexity and variability in composition of water constituents pose major challenges for development of algorithms to estimate biogeochemical data products from optical observations over the continuum of diverse aquatic environments. To address these challenges, we examined an adaptive optical approach that accounts for variability in composition of suspended particulate matter. We use the ratio of particulate organic carbon to suspended particulate matter concentration, POC/SPM, for optically discriminating water bodies with varying proportions of organic and mineral particles. Through the analysis of field data from the western Arctic seas that exhibit a broad range of water composition and optical properties, we developed empirical algorithms to estimate SPM and POC/SPM either from the particulate inherent optical properties (IOPs) of seawater, i.e., the spectral particulate absorption ap(λ) or backscattering bbp(λ) coefficients, or directly from the spectral remote-sensing reflectance of the ocean, Rrs(λ). The capability to retrieve POC/SPM from optical measurements was used to formulate the particle composition-specific algorithms for estimating POC from particulate IOPs or Rrs(λ). The evaluation of algorithms with the development field dataset demonstrates that the algorithm formulations accounting for changes in POC/SPM provide significant improvements in POC estimates along the continuum of optically-complex Arctic waters compared with algorithms that do not account for variations in particulate composition, such as the current standard POC algorithm used for global satellite applications. The results of example application of the particle composition-specific algorithm and standard global algorithm to satellite observations are consistent with comparisons of these algorithms for our field dataset, which supports a conclusion that the standard algorithm tends to overestimate POC in Arctic waters that exhibit a broad range of particle composition. Although this study demonstrates the adaptive approach using data from the Arctic region, it has broader significance and is amenable to further enhancements by including other optically-derivable water constituent properties. Further validation analyses and efforts are needed towards a unified approach with improved representation of cause-and-effect relationships between water composition and optical properties to enable improved optically-based applications across a wide range of water bodies.

Editor's note: Iterative deblending using the POCS algorithm in the approximate flattened domain

Editor's note: Iterative deblending using the POCS algorithm in the approximate flattened domain

Expression of Concern: Iterative deblending using the POCS algorithm in the approximate flattened domain

Expression of Concern: Iterative deblending using the POCS algorithm in the approximate flattened domain

Evaluation of Chlorophyll-a and POC MODIS Aqua Products in the Southern Ocean

The Southern Ocean (SO) is highly sensitive to climate change. Therefore, an accurate estimate of phytoplankton biomass is key to being able to predict the climate trajectory of the 21st century. In this study, MODerate resolution Imaging Spectroradiometer (MODIS), on board EOS Aqua spacecraft, Level 2 (nominal 1 km × 1 km resolution) chlorophyll-a (C S a t ) and Particulate Organic Carbon (POC s a t ) products are evaluated by comparison with an in situ dataset from 11 research cruises (2008–2017) to the SO, across multiple seasons, which includes measurements of POC and chlorophyll-a (C i n s i t u ) from both High Performance Liquid Chromatography (C H P L C ) and fluorometry (C F l u o ). Contrary to a number of previous studies, results highlighted good performance of the algorithm in the SO when comparing estimations with HPLC measurements. Using a time window of ±12 h and a mean satellite chlorophyll from a 5 × 5 pixel box centered on the in situ location, the median C S a t :C i n s i t u ratios were 0.89 (N = 46) and 0.49 (N = 73) for C H P L C and C F l u o respectively. Differences between C H P L C and C F l u o were associated with the presence of diatoms containing chlorophyll-c pigments, which induced an overestimation of chlorophyll-a when measured fluorometrically due to a potential overlap of the chlorophyll-a and chlorophyll-c emission spectra. An underestimation of ∼0.13 mg m − 3 was observed for the global POC algorithm. This error was likely due to an overestimate of in situ POC i n s i t u measurements from the impact of dissolved organic carbon not accounted for in the blank correction. These results highlight the important implications of different in situ methodologies when validating ocean colour products.

Retracted: Iterative deblending using the POCS algorithm in the approximate flattened domain

Retracted: Iterative deblending using the POCS algorithm in the approximate flattened domain

Iterative deblending using the POCS algorithm in the approximate flattened domain

Abstract We proposed an improved method to eliminate the interference generated by simultaneous-source acquisition, which can help shorten the acquisition period and improve the quality of seismic data. An iterative mathematical framework is devised, which uses the projection onto convex sets algorithm to estimate the blending noise subtracted from the pseudo-deblended data to separate the blended data in an iterative way. Differently to the conventional method using the coherent-promoting operator only based on the curvelet transform, we combine the curvelet transform and the approximate flattened operator (AFO) to improve the deblended result, which can flatten seismic events approximately to preserve the details of useful signals. This is the first time that the AFO and the curvelet transform are combined to enhance the effect of the coherent-promoting operator and improve the performance of deblending. To display the advantages of the improved method, we use both simulated synthetic data and field data examples to compare and analyse the deblended results using our method and the conventional method, and confirm that the improved method can perform better.

3D simultaneous seismic data reconstruction and noise suppression based on the curvelet transform

Seismic data contain random noise interference and are affected by irregular subsampling. Presently, most of the data reconstruction methods are carried out separately from noise suppression. Moreover, most data reconstruction methods are not ideal for noisy data. In this paper, we choose the multiscale and multidirectional 2D curvelet transform to perform simultaneous data reconstruction and noise suppression of 3D seismic data. We introduce the POCS algorithm, the exponentially decreasing square root threshold, and soft threshold operator to interpolate the data at each time slice. A weighing strategy was introduced to reduce the reconstructed data noise. A 3D simultaneous data reconstruction and noise suppression method based on the curvelet transform was proposed. When compared with data reconstruction followed by denoizing and the Fourier transform, the proposed method is more robust and effective. The proposed method has important implications for data acquisition in complex areas and reconstructing missing traces.

Simultaneous deblurring and iterative reconstruction of CBCT for image guided brain radiosurgery

One of the limiting factors in cone-beam CT (CBCT) image quality is system blur, caused by detector response, x-ray source focal spot size, azimuthal blurring, and reconstruction algorithm. In this work, we develop a novel iterative reconstruction algorithm that improves spatial resolution by explicitly accounting for image unsharpness caused by different factors in the reconstruction formulation. While the model-based iterative reconstruction techniques use prior information about the detector response and x-ray source, our proposed technique uses a simple measurable blurring model. In our reconstruction algorithm, denoted as simultaneous deblurring and iterative reconstruction (SDIR), the blur kernel can be estimated using the modulation transfer function (MTF) slice of the CatPhan phantom or any other MTF phantom, such as wire phantoms. The proposed image reconstruction formulation includes two regularization terms: (1) total variation (TV) and (2) nonlocal regularization, solved with a split Bregman augmented Lagrangian iterative method. The SDIR formulation preserves edges, eases the parameter adjustments to achieve both high spatial resolution and low noise variances, and reduces the staircase effect caused by regular TV-penalized iterative algorithms. The proposed algorithm is optimized for a point-of-care head CBCT unit for image-guided radiosurgery and is tested with CatPhan phantom, an anthropomorphic head phantom, and 6 clinical brain stereotactic radiosurgery cases. Our experiments indicate that SDIR outperforms the conventional filtered back projection and TV penalized simultaneous algebraic reconstruction technique methods (represented by adaptive steepest-descent POCS algorithm, ASD-POCS) in terms of MTF and line pair resolution, and retains the favorable properties of the standard TV-based iterative reconstruction algorithms in improving the contrast and reducing the reconstruction artifacts. It improves the visibility of the high contrast details in bony areas and the brain soft-tissue. For example, the results show the ventricles and some brain folds become visible in SDIR reconstructed images and the contrast of the visible lesions is effectively improved. The line-pair resolution was improved from 12 line-pair/cm in FBP to 14 line-pair/cm in SDIR. Adjusting the parameters of the ASD-POCS to achieve 14 line-pair/cm caused the noise variance to be higher than the SDIR. Using these parameters for ASD-POCS, the MTF of FBP and ASD-POCS were very close and equal to 0.7 mm−1 which was increased to 1.2 mm−1 by SDIR, at half maximum.

Remote sensing of particulate organic carbon dynamics in a eutrophic lake (Taihu Lake, China)

A two step approach was developed to estimate POC concentrations in a shallow eutrophic lake, where dissolved carbon and inorganic particulate matter strongly influence optical properties. The approach was based on the identification of a co-variability of POC concentrations with one or more inherent optical properties (IOPs). In the present case, a relationship between POC and absorption at 620nm was identified and was combined with a reflectance based algorithm for case II waters. Compared with other published POC algorithms, this approach provided better estimates over a wide range of POC concentrations in two hypereutrophic lake waters with the predominance of cyanobacteria (R2>0.80). While it is unlikely that a single POC algorithm can be created that is independent of the variability of POC sources, this approach allows for a common method to identify appropriate algorithms with which POC dynamics can be explored.

Super Resolution and Denoising Model Research on TOF Camera

At present, the three-dimension imaging technology has developed rapidly, in monitoring, identification, navigationand other fields having been widely used. The SR4000 camera is a 3D imaging equipment which is based on TOF(Time of flight) technology.It is a kind of new, small 3D imaging equipment , which can capture gray, depth, and amplitude image at the same time. It has more incomparable advantages than traditional cameras. TOF three-dimension imaging technology has become a research hotspot at home and abroad.But the TOF camera also has its own defects.The resolution is low. And it is more easily affected by noise and surroundings . This paper proposes a kind of algorithm of super resolution reconstruction based on pocs. Furthermore, improve the pocs algorithm. This method has good improvement effect.In order to further improve the quality of the distance images, wavelet denoising is adopted on the basis of super resolution reconstruction .So, it has a good improvement for the captured distance images.

An Improved Algorithm for Super-Resolution Video

Aiming at the characteristics of the UAV camera, camera data nowadays, a new improved method is proposed based on putting the low-resolution video reconstruction into high-resolution video. First, the low-resolution video frame is done spectrum analysis by Fourier transform. Second, find the maximum gradient descent point to determine the cut off frequency. Finally making use of high-resolution images with high frequency detail, then motion compensated. Through POCS algorithm, then iterated, obtaining super-resolution reconstruction video and realizing the above by MATLAB simulation.

Compact Compound Imaging System with Large Target Surface

Compact compound imaging system with large CCD target surface has been researched. The target surface reaches 50mmX50mm, and reduces the system processing and installation difficulty. We present the system overall plan and discuss the imaging characteristics. The imaging results for sector resolution target show that the system can image with multi channel crosstalk free. The results show that whether the single convex lens system or double convex lens system, the definition and contrast of single channel graphs are poor. However we can get the super resolution image by POCS algorithm. The reconstructed results show that image quality has been obviously improved compared with the single channel image, which proof the availability of the compact compound imaging system with large CCD target surface. Key words: Compact ompound imaging system, Microlens array, Doublet lens, POCS

WE‐G‐217BCD‐06: Fully Incorporated Scanning Geometry for Improved Accuracy in C‐Arm CBCT Image Reconstruction

The adoption of C-arm cone-beam CT (CBCT) can minimize interruption to the interventional workflow. However, C-arm gantry rotation can be susceptible to gravity and mechanical instability, therefore resulting images with contamination of tilting and wobbling motion. The purpose of this work is to investigate and demonstrate potential improvement of C-arm CBCT images by tailoring and applying an innovative iterative algorithm that can fully incorporate accurate scanning geometry. A clinical C-arm CBCT system (Toshiba Infinix-i, Japan) was used for collecting projection data of a patient's brain with aneurysm. A mask scan was performed before contrast was injected, and a contrast scan was performed during continuous contrast feeding. During each scan, 108 projections were acquired over a range of approximately 200 degrees within about 4.6 seconds. Logarithm subtraction was then carried out view by view to obtain 108 projections of virtually only the vasculature. An iterative algorithm, referred to as the ASD-POCS algorithm, was modified to fully incorporate calibration data characterizing the actual scan geometry that deviates from a circular trajectory due to gantry tilting and wobbling. We then applied both FDK and ASD-POCS algorithms to reconstructing image from the acquired patient data. In FDK reconstructions without considering C-arm tilting and wobbling motion, artifacts can be observed, which break vessel continuity and compromise image's clinical utility. We then performed FDK reconstructions by incorporating the system geometry in the back-projection step and obtained improved vessel continuity. Finally we applied the ASD- POCS algorithm by incorporating calibration data in the forward- and back- projector, and obtained reconstructions with further improvement on the recovery of small, secondary vascular branches. Appropriately developed iterative algorithms can improve C-arm CBCT image quality by fully incorporating scanning geometry, and can potentially enable spatial-resolution demanding applications which can be challenging to achieve with the FDK algorithm.

An Algorithms for Super-Resolution Reconstruction of Video Based on Spatio-Temporal Adaptive

For the weakness of conventional POCS algorithms, a novel spatio-temporal adaptive super-resolution reconstruction algorithm of video is proposed in this paper. The spatio-temporal adaptive mechanism, which is based on POCS super-resolution reconstruction algorithm, can effectively prevent reconstructed image from the influence of inaccuracy of motion information and avoid the impact of noise amplification, which exist in using conventional POCS algorithms to reconstruct image sequences in dramatic motion. Experimental results show that the spatio-temporal adaptive algorithm not only effectively alleviate amplification noise but is better than the traditional POCS algorithms in signal to noise ration.

Tiny a priori knowledge solves the interior problem in computed tomography

Based on the concept of differentiated backprojection (DBP) (Noo et al 2004 Phys. Med. Biol. 49 3903, Pan et al 2005 Med. Phys. 32 673, Defrise et al 2006 Inverse Problems 22 1037), this paper shows that the solution to the interior problem in computed tomography is unique if a tiny a priori knowledge on the object f(x, y) is available in the form that f(x, y) is known on a small region located inside the region of interest. Furthermore, we advance the uniqueness result to obtain more general uniqueness results which can be applied to a wider class of imaging configurations. We also develop a reconstruction algorithm which can be considered an extension of the DBP-POCS (projection onto convex sets) method described by Defrise et al (2006 Inverse Problems 22 1037), where we not only extend this method to the interior problem but also introduce a new POCS algorithm to reduce computational cost. Finally, we present experimental results which show evidence that the inversion corresponding to each obtained uniqueness result is stable.

Relationships between the surface concentration of particulate organic carbon and optical properties in the eastern South Pacific and eastern Atlantic Oceans

Abstract. We have examined several approaches for estimating the surface concentration of particulate organic carbon, POC, from optical measurements of spectral remote-sensing reflectance, Rrs(λ), using field data collected in tropical and subtropical waters of the eastern South Pacific and eastern Atlantic Oceans. These approaches include a direct empirical relationship between POC and the blue-to-green band ratio of reflectance, Rrs(λB)/Rrs(555), and two-step algorithms that consist of relationships linking reflectance to an inherent optical property IOP (beam attenuation or backscattering coefficient) and POC to the IOP. We considered two-step empirical algorithms that exclusively include pairs of empirical relationships and two-step hybrid algorithms that consist of semianalytical models and empirical relationships. The surface POC in our data set ranges from about 10 mg m−3 within the South Pacific Subtropical Gyre to 270 mg m−3 in the Chilean upwelling area, and ancillary data suggest a considerable variation in the characteristics of particulate assemblages in the investigated waters. The POC algorithm based on the direct relationship between POC and Rrs(λB)/Rrs(555) promises reasonably good performance in the vast areas of the open ocean covering different provinces from hyperoligotrophic and oligotrophic waters within subtropical gyres to eutrophic coastal upwelling regimes characteristic of eastern ocean boundaries. The best error statistics were found for power function fits to the data of POC vs. Rrs(443)/Rrs(555) and POC vs. Rrs(490)/Rrs(555). For our data set that includes over 50 data pairs, these relationships are characterized by the mean normalized bias of about 2% and the normalized root mean square error of about 20%. We recommend that these algorithms be implemented for routine processing of ocean color satellite data to produce maps of surface POC with the status of an evaluation data product for continued work on algorithm development and refinements. The two-step algorithms also deserve further attention because they can utilize various models for estimating IOPs from reflectance, offer advantages for developing an understanding of bio-optical variability underlying the algorithms, and provide flexibility for regional or seasonal parameterizations of the algorithms.

A CQPOCS Algorithm for Fast Feature Point Matching

A clustering and quadratic programming based projection onto convex sets(CQPOCS)algorithm for fast feature point matching is presented in this paper．Via feature point clustering,the problem of matching model point set and taget point set is converted in to the problem of matching corresponding clusters,thus reducing the computational cost．Then,quatratic programming based POCS algorithm is used to solve the cluster matching problem without incurring the successive POCS algorithm’s accumulating deviation due to successive projections onto row convex sets and column convex sets．Simulation results show that our CQPOCS algorithm has satisfactory matching accuracy and computational safety．

Optimal reconstruction of images from localized phase

The importance of localized phase in signal representation is investigated. The convergence rate of the POCS algorithm (projection onto convex sets) used for image reconstruction from spectral phase is defined and analyzed, and the characteristics of images optimally reconstructed from phase-only information are presented. It is concluded that images of geometric form are most efficiently reconstructed from their spectral phase, whereas images of symmetric form have the poorest convergence characteristics. The transition between the two extremes is shown to be continuous. The results provide a new approach and analysis of the previously reported advantages of the localized phase representation over the global approach, and suggest possible compression schemes.

Quantitative evaluation of several partial fourier reconstruction algorithms used in mri

Partial Fourier reconstruction algorithms exploit the redundancy in magnetic resonance data sets so that half of the data is calculated during image reconstruction rather than acquired. The conjugate synthesis, Margosian, homodyne detection, Cuppen and POCS algorithms are evaluated using spatial frequency domain analysis to show their characteristics and where limitations may occur. The phase correction used in partial Fourier reconstruction is equivalent to a convolution in the frequency domain and the importance of accurately implementing this convolution is demonstrated. New reconstruction approaches, based on passing the partial data through a phase correcting, finite impulse response (FIR), digital filter are suggested. These FIR and MoFIR algorithms have a speed near that of the Margosian and homodyne detection reconstructions, but with a lower error; close to that of the Cuppen/POCS iterative approaches. Quantitative analysis of the partial Fourier algorithms, tested with three phase estimation techniques, are provided by comparing artificial and clinical data reconstructed using full and partial Fourier techniques.