Local Pixel Grouping Research Articles

Doppler Ultrasound under Image Denoising Algorithm in the Diagnosis and Treatment of Fetal Growth Restriction Using Aspirin Combined with Low-Molecular-Weight Heparin.

Objective This study explored the clinical application value of image denoising algorithm combined with Doppler ultrasound imaging in evaluation of aspirin combined with low-molecular-weight heparin (LMWH) on fetal growth restriction (FGR). Method A two-stage image denoising by principal component analysis (PCA) with local pixel grouping (LPG-PCA) denoising algorithm was constructed in this study. Eighty FGR pregnant women were included in the study, and they were rolled into an experimental group (aspirin enteric-coated tablets + LMWH calcium injection) and a control group (LMWH calcium injection) according to the different treatment plans, with 40 cases in each group. All patients were performed with Doppler ultrasound imaging. The blood flow parameters (BFPs) were recorded and compared before and after the treatment in two groups, including power index (PI), resistance index (RI), high systolic blood flow velocity (S), high diastolic blood flow velocity (D), S/D value, and peak systolic velocity (PSV). In addition, the middle cerebral artery (MCA) BFPs, cerebral placental rate (CPR), amniotic fluid index (AFI) and perinatal outcome (PO) of the two groups were compared. Results The total effective rate of treatment in group A (87.5%) was greatly higher than that in group B (62.5%), showing statistical difference (P < 0.05). The PI (0.72 ± 0.19), RI (0.57 ± 0.17), and S/D values (2.26 ± 0.43) in group A were dramatically lower than those in group B, which were 0.92 ± 0.21, 0.75 ± 0.14, and 2.64 ± 0.45, respectively (P < 0.05), and the AFI was higher (13.71 ± 2.2 cm vs 11.38 ± 2.16 cm) (P < 0.05). The Apgar score (9.17 ± 0.26), weight (3.57 ± 1.08), and gestational age (38.85 ± 2.50) of group A were all higher in contrast to those of group B, which were 7.33 ± 0.25, 2.61 ± 1.13, and 36.18 ± 2.25, respectively (P < 0.05). In addition, the fetal double parietal diameter (2.4 ± 0.9 mm), femur diameter (2.2 ± 0.6 mm), head circumference (1.2 ± 0.4 mm), abdominal circumference (1.3 ± 0.7 mm), and uterine height (0.8 ± 0.3 mm) in group A were obviously superior to those in group B, which were 1.8 ± 0.4 mm, 1.7 ± 0.5 mm, 0.8 ± 0.2 mm, 0.9 ± 0.4 mm, and 0.4 ± 0.6 mm, respectively, showing statistically observable differences (P < 0.05). Conclusion Doppler ultrasound based on image denoising algorithm can accurately evaluate the effect of aspirin combined with LMWH on the improvement of FGR and showed good application value.

SAR image de-noising via grouping-based PCA and guided filter

A novel synthetic aperture radar (SAR) image de-noising method based on the local pixel grouping (LPG) principal component analysis (PCA) and guided filter is proposed. This method contains two steps. In the first step, we process the noisy image by coarse filters, which can suppress the speckle effectively. The original SAR image is transformed into the additive noise model by logarithmic transform with deviation correction. Then, we use the pixel and its nearest neighbors as a vector to select training samples from the local window by LPG based on the block similar matching. The LPG method ensures that only the similar sample patches are used in the local statistical calculation of PCA transform estimation, so that the local features of the image can be well preserved after coefficients shrinkage in the PCA domain. In the second step, we do the guided filtering which can effectively eliminate small artifacts left over from the coarse filtering. Experimental results of simulated and real SAR images show that the proposed method outstrips the state-of-the-art image de-noising methods in the peak signal-to-noise ratio (PSNR), the structural similarity (SSIM) index and the equivalent number of looks (ENLs), and is of perceived image quality.

Noise suppressed and bias field corrected image segmentation method for porous Ni-YSZ anode microstructure

Accurate three-phase identification from Solid Oxide Fuel Cell (SOFC) anode micrograph is challenged by both noise and intensity inhomogeneity. In this paper, a novel framework is proposed for porous Ni-YSZ cermet anode Optical Microscopy (OM) image segmentation. The proposed framework takes advantage of a statistical model in which an observed image is decomposed into two multiplicative components (bias field and true image) and one additive component (noise). A two-stage Principal Component Analysis with Local Pixel Grouping (LPG-PCA) denoising algorithm is firstly performed to suppress additive noise, it can preserve more image local structural features by modeling a pixel and its nearest neighbors as a vector variable and selecting training samples with similar contents to this variable in a local window for PCA transformation. In order to enhance the robustness to noise, uneven illumination and other outliers, a kernel metric is introduced into fuzzy clustering method embedded with bias field correction for image segmentation. The proposed method has been compared to other state-of-the-art segmentation algorithms on both simulated images and real SOFC anode OM images. Extensive experiments results have demonstrated that the proposed framework can successfully eliminate the influence of both uneven illumination and noise on real Ni/YSZ anode OM images to obtain a better three-phase identification accuracy. The high-quality segmentation results lay firm foundation for accurate microstructural parameter extraction.

An Enhanced Low Rank Approximation SVD-Based Method for Image Denoising

Nonlocal self-similarity of images has attracted considerable interest in the field of image processing and has led to several state-of-the-art image denoising algorithms, such as block matching and 3-D, principal component analysis with local pixel grouping, patch-based locally optimal wiener, and spatially adaptive iterative singular-value thresholding. In this paper, we propose a computationally simple denoising algorithm using the nonlocal self-similarity and the low-rank approximation (LRA). The proposed method consists of three basic steps. First, our method classifies similar image patches by the block-matching technique to form the similar patch groups, which results in the similar patch groups to be low rank. Next, each group of similar patches is factorized by singular value decomposition (SVD) and estimated by taking only a few largest singular values and corresponding singular vectors. Finally, an initial de-noised image is generated by aggregating all processed patches. For low-rank matrices, SVD can provide the optimal energy compaction in the least square sense. The proposed method exploits the optimal energy compaction property of SVD to lead an LRA of similar patch groups. Unlike other SVD based methods, the LRA in SVD domain avoids learning the local basis for representing image patches, which usually is computationally expensive. The experimental results demonstrate that the proposed method can effectively reduce noise and be competitive with the current state-of-the-art denoising algorithms in terms of both quantitative metrics and subjective visual quality.

Hybrid SAR Speckle Reduction Using Complex Wavelet Shrinkage and Non-Local PCA-Based Filtering

In this paper, a new hybrid despeckling method, based on Undecimated Dual-Tree Complex Wavelet Transform (UDT-CWT) using maximum a posteriori (MAP) estimator and non-local Principal Component Analysis (PCA)-based filtering with local pixel grouping (LPG-PCA), was proposed. To achieve a heterogeneous-adaptive speckle reduction, SAR image is classified into three classes of point targets, details, or homogeneous areas. The despeckling is done for each pixel based on its class of information. Logarithm transform was applied to the SAR image to convert the multiplicative speckle into additive noise. Our proposed method contains two principal steps. In the first step, denoising was done in the complex wavelet domain via MAP estimator. After performing UDT-CWT, the noise-free complex wavelet coefficients of the log-transformed SAR image were modeled as a two-state Gaussian mixture model. Furthermore, the additive noise in the complex wavelet domain was considered as a zero-mean Gaussian distribution. In the second step, after applying inverse UDT-CWT, an iterative LPG-PCA method was used to smooth the homogeneous areas and enhance the details. The proposed method was compared with some state-of-the-art despeckling methods. The experimental results showed that the proposed method leads to a better speckle reduction in homogeneous areas while preserving details.

A comparison between PCA and some enhancement filters for denoising astronomical images

This paper includes a comparison between denoising techniques by using statistical approach, principal component analysis with local pixel grouping (PCA-LPG), this procedure is iterated second time to further improve the denoising performance, and other enhancement filters were used. Like adaptive Wiener low pass-filter to a grayscale image that has been degraded by constant power additive noise, based on statistics estimated from a local neighborhood of each pixel. Performs Median filter of the input noisy image, each output pixel contains the Median value in the M-by-N neighborhood around the corresponding pixel in the input image, Gaussian low pass-filter and Order-statistic filter also be used.&#x0D; Experimental results shows LPG-PCA method gives better performance, especially in image fine structure preservation, compared with other general denoising algorithms.

A Hybrid Technique of Image Denoising Using the Curvelet Transform based Denoising Method and Two-stage Image Denoising by PCA with Local Pixel Grouping

A Hybrid Technique of Image Denoising Using the Curvelet Transform based Denoising Method and Two-stage Image Denoising by PCA with Local Pixel Grouping

An Efficient SVD-Based Method for Image Denoising

Nonlocal self-similarity of images has attracted considerable interest in the field of image processing and has led to several state-of-the-art image denoising algorithms, such as block matching and 3-D, principal component analysis with local pixel grouping, patch-based locally optimal wiener, and spatially adaptive iterative singular-value thresholding. In this paper, we propose a computationally simple denoising algorithm using the nonlocal self-similarity and the low-rank approximation (LRA). The proposed method consists of three basic steps. First, our method classifies similar image patches by the block-matching technique to form the similar patch groups, which results in the similar patch groups to be low rank. Next, each group of similar patches is factorized by singular value decomposition (SVD) and estimated by taking only a few largest singular values and corresponding singular vectors. Finally, an initial denoised image is generated by aggregating all processed patches. For low-rank matrices, SVD can provide the optimal energy compaction in the least square sense. The proposed method exploits the optimal energy compaction property of SVD to lead an LRA of similar patch groups. Unlike other SVD-based methods, the LRA in SVD domain avoids learning the local basis for representing image patches, which usually is computationally expensive. The experimental results demonstrate that the proposed method can effectively reduce noise and be competitive with the current state-of-the-art denoising algorithms in terms of both quantitative metrics and subjective visual quality.

Dual Polynomial Thresholding For Transform Denoising In Application To Local Pixel Grouping Method

Thresholding operators have been used successfully for denoising signals, mostly in the wavelet domain. These operators transform a noisy coefficient into a denoised coefficient with a mapping that depends on signal statistics and the value of the noisy coefficient itself. This paper demonstrates that a polynomial threshold mapping can be used for enhanced denoising of Principal Component Analysis (PCA) transform coefficients. In particular, two polynomial threshold operators are used here to map the coefficients obtained with the popular local pixel grouping method (LPG-PCA), which eventually improves the denoising power of LPG-PCA. The method reduces the computational burden of LPG-PCA, by eliminating the need for a second iteration in most cases. Quality metrics and visual assessment show the improvement.

Improvement to Blind Image Denoising by Using Local Pixel Grouping with SVD

Image denoising without any idea about kind of noise is very cumbersome as compared to known noise. This paper proposed a novel technique for blind image denoising using SVD and local pixel grouping. The technique is checked against salt & pepper noise and Gaussian noise for gray as well as color images and compared with state of art algorithm LPGPCA. It is found that the proposed technique gives better results when compared using objective criteria.

A new approach for edge detection in noisy images based on the LPGPCA technique

In this study, a new approach to edge detection on images, corrupted with Gaussian and impulsive noise, is presented. The concept, under the decomposition of image with its principal component being an analysis on local pixel grouping for noise suppression, called LPGPCA based denoising, is adopted in order to obtain noiseless gradient maps for edge extraction. As a result, an algorithm has been developed called LPGPCA-ED. Firstly, horizontal and vertical gradient images are computed; then the gradient images are decomposed into a noiseless phase by applying the LPGPCA algorithm. Once a single gradient map has been obtained, a smart nonmaximum suppression operation is carried out to obtain a binary edge map. To show the accuracy of the proposed edge detector objectively, F-measure and PFOM results of the proposed edge detector on images with different Gaussian and impulsive noise are compared with the results of traditional and certain recently published edge detectors. Objectively, the experimental results on RUG and receiver operating characteristic (ROC) curve databases show that our method has better performance than other corresponding edge detectors. Moreover, subjective experiments on a variety of noise contaminated images show that the LPGPCA-ED algorithm is more robust under high level noise conditions, and is able to reveal well-linked lines and also preserve the structural form of a processed image.

Integrating Local Pixel Grouping with Dual Tree Complex Wavelet Packets for De-Noising of Medical Images

Integrating Local Pixel Grouping with Dual Tree Complex Wavelet Packets for De-Noising of Medical Images

쿼드트리 기반의 다중 스케일 블록 영역 검출기를 통한 구간적 영상 잡음 제거 기법

This paper presents a piecewise image denoising with multi-scale block region detector based on quadtree structure for effective image restoration. Proposed piecewise image denoising method suggests multi-scale block region detector (MBRD) by dividing whole pixels of a noisy image into three parts, with regional characteristics: strong variation region, weak variation region, and flat region. These regions are classified according to total pixels variation between multi-scale blocks and are applied principal component analysis with local pixel grouping, bilateral filtering, and structure-preserving image decomposition operator called relative total variation. The performance of proposed method is evaluated by Experimental results. we can observe that region detection results generated by the detector seems to be well classified along the characteristics of regions. In addition, the piecewise image denoising provides the positive gain with regard to PSNR performance. In the visual evaluation, details and edges are preserved efficiently over the each region; therefore, the proposed method effectively reduces the noise and it proves that it improves the performance of denoising by the restoration process according to the region characteristics.

Defect Detection Improvement of Digitised Radiographs by Principal Component Analysis with Local Pixel Grouping

Radiographic inspection is one of the most important techniques among non-destructive testing methods. Radiographic images are often very noisy and the image quality and the interpreter’s experience can affect the inspection of radiographs and their evaluation. In this research, principal component analysis (PCA) with local pixel grouping (LPG) algorithms was used for image enhancement for radiograph image interpretation. In this method, a pixel and its neighbors are considered as a vector variable for preservation of radiography image local structure. This method is a statistical method that uses an orthogonal property to transform and convert a set of observations of possibly correlated variables into a set of values of uncorrelated variables. Here, the PCA-LPG denoising algorithm has been applied to radiographic images with different defects to obtain denoised images. The results show that the contrast of denoised radiography images is better than the original image and the defects are much clearer. Also, the evaluation of the image quality enhancement show the contrast to noise level increases almost two times by the proposed PCA-LPG method.

Laser Active Image-Denoising Based on Principal Component Analysis with Local Pixel Grouping

Denoising is an important issue for laser active image. This paper attempted to process laser active image in the low-dimensional sub-space. We adopted the principal component analysis with local pixel grouping (LPG-PCA) denoising method proposed by Zhang [1], and compared it with the conventional denoising method for laser active image, such as wavelet filtering, wavelet soft threshold filtering and median filtering. Experimental results show that the image denoised by LPG-PCA has higher BIQI value than other images, most of the speckle noise can be reduced and the detail structure information is well preserved. The low-dimensional sub-space idea is a new direction for laser active image denoising.

Noise Removal in Image Using LPG-PCA (Local Pixel GroupingPrinciple Component Analysis) Algorithm

In this paper an effective algorithm for noise removal in an image is obtained by using PCA (principal component analysis) with LPG (Local Pixel Grouping). This technique ensures the preservation of image local structure. Here the pixels and its neighbors are treated as vector variables whose training samples are selected from local windows using block matching based LPG. This ensures only the similar samples are selected for the PCA transformation so that the desired local characteristics are only preserved with considerable noise reduction. The LPG -PCA algorithm is performed twice to enhance the quality of an image. The first iteration would remove the noise considerably and the second iteration would preserve the image features like edges etc. The LPG-PCA algorithm will adaptively adjust the noise level of an image unlike WT (Wavelet Transformation). Several experimental results show the effectiveness of the proposed algorithm.

Gray Scale Image Denoising using PCA-SPT

This paper proposed a spatially adaptive image denoising scheme, which is comprised of two stages. In the first stage, image is denoised by using Principal Component Analysis (PCA) with Local Pixel Grouping (LPG). LPG-PCA can effectively preserve the image fine structures while denoising. In the second stage, we use Steerable Pyramid Transform (SPT) to decompose images into frequency sub-bands. The noise level is updated adaptively before second stage denoising. Steerable Pyramid Transform in the second stage further improves the denoising performance. This paper also reviews on the present denoising processes and performs their comparative study. Experimental results demonstrate that the proposed PCA-SPT algorithm achieve competitive outcomes. PCA-SPT works well in image fine structure preservation, compared with state-of-the-art denoising algorithms. Keywords: AWGN; Wavelet; SPT; LPG-PCA; BM3D; Edge preservation.

Comprehensive analysis of LPG-PCA algorithms in denoising and deblurring of medical images

This article presents the detailed analysis of the local pixel grouping–principle component analysis (LPG-PCA) algorithm in denoising and deblurring of medical images. Inefficient diagnosis of the medical images containing lot of information is often affected by the noise and artifacts. In order to remove these noises and artifacts, a statistical decorrelation technique, LPG-PCA is used which is found to be one of the efficient methods, which could be used in improving the performance of medical images. For better preservation of local structures of the image, a pixel and its nearest neighbors are modeled as a vector variable, which leads to the selection of similar intensity characteristics. Denoising method used in this article is done in two stages for improving the denoising performance. The smoothening caused by the denoising process is removed by using LPG-PCA along with adaptive sparse domain representations in the deblurring process. This involves clustering of data and finding the subdictionary of each cluster using LPG-PCA. Experimental results show that an average improvement of 2.9 and 5.1 dB is found in the computed tomography and magnetic resonance imaging images using denoising and deblurring process. © 2013 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 23, 157–170, 2013

PCA based image denoising

Principal component analysis (PCA) is an orthogonal transformation that seeks the directions of maximum variance in the data and is commonly used to reduce the dimensionality of the data. In image denoising, a compromise has to be found between noise reduction and preserving significant image details. PCA is a statistical technique for simplifying a dataset by reducing datasets to lower dimensions. It is a standard technique commonly used for data reduction in statistical pattern recognition and signal processing. This paper proposes a denoising technique by using a new statistical approach, principal component analysis with local pixel grouping (LPG). This procedure is iterated second time to further improve the denoising performance, and the noise level is adaptively adjusted in the second stage.

Two-stage image denoising by principal component analysis with local pixel grouping

This paper presents an efficient image denoising scheme by using principal component analysis (PCA) with local pixel grouping (LPG). For a better preservation of image local structures, a pixel and its nearest neighbors are modeled as a vector variable, whose training samples are selected from the local window by using block matching based LPG. Such an LPG procedure guarantees that only the sample blocks with similar contents are used in the local statistics calculation for PCA transform estimation, so that the image local features can be well preserved after coefficient shrinkage in the PCA domain to remove the noise. The LPG-PCA denoising procedure is iterated one more time to further improve the denoising performance, and the noise level is adaptively adjusted in the second stage. Experimental results on benchmark test images demonstrate that the LPG-PCA method achieves very competitive denoising performance, especially in image fine structure preservation, compared with state-of-the-art denoising algorithms.