Block-Based Compressed Sensing for Neutron Radiation Image Using WDFB

The key of image denoising algorithms is to preserve the details of the original image while denoising the noise in the image. The existing algorithms use the external information to better preserve the details of the image, but the use of external information needs the support of similar images or image patches. In this paper, an edge-aware image denoising algorithm is proposed to achieve the goal of preserving the details of original image while denoising and using only the characteristics of the noisy image. In general, image denoising algorithms use the noise prior to set parameters todenoise the noisy image. In this paper, it is found that the details of original image can be better preserved by combining the prior information of noise and the image edge features to set denoising parameters. The experimental results show that the proposed edge-aware image denoising algorithm can effectively improve the performance of block-matching and 3D filtering and patch group prior-based denoising algorithms and obtain higher peak signal-to-noise ratio and structural similarity values.

Implementation of compressive sensing in ECG and EEG signal processing

This paper introduces a compressive sampling (CS) scheme for pulse stream images. We adopt a particular sparse sampling matrix, that we call Radon-like CS matrix. Whilst the Radon transform evaluates projections of a given image along different directions, the Radon-like CS matrix evaluates randomly weighted projections of the image along a few directions. We demonstrate that the such CS measurements are invertible and assess the reconstruction accuracy of CS with the Radonlike sampling matrix by numerical trials. The Radon-like CS performs almost as well as state of the art techniques, with a reduced number of operations. As an application example, we show that, when implemented in a resource limited framework such as a Smart Sensors Grid, the sampling matrix significantly reduces inter-node signaling and then the associated energy consumption. © 2012 IEEE.

The recently proposed compressive sensing (CS) theory provides a new solution for multiple description coding (MDC) with fine granularity, by treating each random CS measurement as a description. The performance of CS-based MDC (CS-MDC) depends on the efficacy of the CS recovery algorithm. Existing CS recovery algorithms recover the signal in a fixed space (e.g., Wavelet, DCT, and gradient spaces) for the entire duration of the signal, even though a typical multimedia signal exhibits sparsity in time/space variant spaces. To rectify this problem and develop a better CS recovery algorithm for CSMDC, we propose a learning-based framework to conduct the CS recovery in locally adaptive spaces, and carry out a case study on image MDC. A set of prior image models are learned offline from a training set to facilitate the CS recovery in local adaptive bases. Experiments show that the learning-based CS recovery algorithm can significantly improve the performance of the previous CS-MDC technique in both PSNR and visual quality.

This paper proposes a surface fitting algorithm for inspection of flat panel display(FPD) based on multilevel B-spline approximation(MBA). FPD devices have non-uniform background, and shape and intensity of defects are very various. MBA is useful algorithm for surface fitting but it is sensitive to number of control lattices. As level increases, details of original image can be reconstructed. To detect defect, defective area must not be well-reconstructed. In this paper, we propose a level decision method of MBA for FPD. After process of each level, we decide whether stop to increase level of MBA or not with simple distance measure. Experimental result shows that our proposed method makes to detect defects easy.

The solutions of equation-based noise detector for an adaptive median filter

Nonuniform support recovery from noisy random measurements by Orthogonal Matching Pursuit

Impulse noise reduction has been a challenge for Image Processing researchers for many decades. Median filter had been the most popular denoising methods. However, owing to its edge jittering and removing image detail properties, numerous efforts have been taken in order to improve its performance. Median ϵ-filter is one of the modified median filters. Median ϵ-filter and median filter behave in a like manner in the presence of noise. Thus, it has all disadvantageous things of median filter. In this article, we improve median ϵ-filter's efficiency by giving weights to the outcomes of ϵ-filter. The experimental results show the excellent performance of our method. Other than that, in the absence of noise, our method preserves the original image's details more than others.

Compressive sensing (CS) has been proposed for images that are sparse under a certain transform domain. However, many natural images are not strictly sparse in the transform domain, causing a tail-folding effect that degrades the performance of the CS reconstruction. To decrease such effect, a sparse-filtering (SF) in Directional Lifting Wavelet Transform (DLWT) domain based Bayesian compressive sensing (BCS) algorithm (DLWT-SF-TSW-BCS) is proposed. At the encoder, DLWT, an efficient multi-scale geometrical analysis (MGA) tool, is used to produce the sparse representation for natural images. Then sparse-filtering is adopted to cut off the small DLWT coefficients before random measurement. At the decoder, the interscale tree-structure redundancy in DLWT domain is further exploited in Bayesian reconstruction. Experimental results show that the proposed DLWT-SF-TSW-BCS algorithm significantly outperforms other state-of-the-art CS reconstruction algorithms, for example, peak signal to noise ratio (PSNR) gain up to 10.00 dB over the tree structured wavelet compressive sensing (TSW-CS).

Recently, compressed sensing (CS) has been widely used in ground penetrating radar (GPR) imaging due to its low sampling time, high quality performance and high quality underground imaging results. There are always a large number of matrix operations involved in CS based GPR imaging process, which takes a long time. Traditionally, all scanning area was imaged by this CS imaging method. However, with the imaging scene becoming larger, the measurement matrix and the data dictionary in CS imaging model expand, which increases the time of CS-based imaging process. Thus, this paper proposes a fast CS based GPR imaging method to reduce the imaging time. Specifically, the geometric characteristics of the B-scan image can firstly be used to extract the region of interest (ROI) where the target may exist in a layered medium. Then the GPR imaging based on CS can be realized in ROI, which can reduce the imaging area and imaging time. Experimental results present that the method can effectively reduce the time of GPR imaging based on CS.

The aims of this study were to develop an accelerated multiparametric magnetic resonance imaging method based on 3D-quantification using an interleaved Look-Locker acquisition sequence with a T2 preparation pulse (3D-QALAS) combined with compressed sensing (CS) and to evaluate the effect of CS on the quantitative mapping, tissue segmentation, and quality of synthetic images. A magnetic resonance imaging system phantom, containing multiple compartments with standardized T1, T2, and proton density (PD) values; 10 healthy volunteers; and 12 patients with multiple sclerosis were scanned using the 3D-QALAS sequence with and without CS and conventional contrast-weighted imaging. The scan times of 3D-QALAS with and without CS were 5:56 and 11:11, respectively. For healthy volunteers, brain volumetry and myelin estimation were performed based on the measured T1, T2, and PD. For patients with multiple sclerosis, the mean T1, T2, PD, and the amount of myelin in plaques and contralateral normal-appearing white matter (NAWM) were measured. Simple linear regression analysis and Bland-Altman analysis were performed for each metric obtained from the datasets with and without CS. To compare overall image quality and structural delineations on synthetic and conventional contrast-weighted images, case-control randomized reading sessions were performed by 2 neuroradiologists in a blinded manner. The linearity of both phantom and volunteer measurements in T1, T2, and PD values obtained with and without CS was very strong (R2 = 0.9901-1.000). The tissue segmentation obtained with and without CS also had high linearity (R2 = 0.987-0.999). The quantitative tissue values of the plaques and NAWM obtained with CS showed high linearity with those without CS (R2 = 0.967-1.000). There were no significant differences in overall image quality between synthetic contrast-weighted images obtained with and without CS (P = 0.17-0.99). Multiparametric imaging of the whole brain based on 3D-QALAS can be accelerated using CS while preserving tissue quantitative values, tissue segmentation, and quality of synthetic images.

In remote sensing applications and medical imaging, one of the key points is the acquisition, real-time preprocessing and storage of information. Due to the large amount of information present in the form of images or videos, compression of these data is necessary. Compressed sensing is an efficient technique to meet this challenge. It consists in acquiring a signal, assuming that it can have a sparse representation, by using a minimum number of nonadaptive linear measurements. After this compressed sensing process, a reconstruction of the original signal must be performed at the receiver. Reconstruction techniques are often unable to preserve the texture of the image and tend to smooth out its details. To overcome this problem, we propose, in this work, a compressed sensing reconstruction method that combines the total variation regularization and the non-local self-similarity constraint. The optimization of this method is performed by using an augmented Lagrangian that avoids the difficult problem of nonlinearity and nondifferentiability of the regularization terms. The proposed algorithm, called denoising-compressed sensing by regularization (DCSR) terms, will not only perform image reconstruction but also denoising. To evaluate the performance of the proposed algorithm, we compare its performance with state-of-the-art methods, such as Nesterov’s algorithm, group-based sparse representation and wavelet-based methods, in terms of denoising and preservation of edges, texture and image details, as well as from the point of view of computational complexity. Our approach permits a gain up to 25% in terms of denoising efficiency and visual quality using two metrics: peak signal-to-noise ratio (PSNR) and structural similarity (SSIM).

Compressed Sensing (CS) theory has gained so much attention recently in the areas of signal processing. The sparsity of the transform coefficients has been widely employed in the early CS recovery techniques. However, except for the sparsity, there are other priors about transform coefficients such as the tree structure and the statistical dependencies that could be employed in CS reconstruction. In this paper, we propose to introduce the Gaussian Scale Mixtures (GSM) model into the tree structure based Orthogonal Matching Pursuit (TSOMP) reconstruction algorithm. This GSM model can efficiently denote the statistical dependencies between wavelet coefficients. And these statistical dependencies will improve the accuracy of the searching of the tree structure subspace in TSOMP algorithm. When both the inter-scale dependences (such as GSM model) of the coefficients and the intra-scale dependences (such as tree structure) of the coefficients are combined into the Orthogonal Matching Pursuit reconstruction algorithm, the noise and instability in TSOMP reconstruction are well reduced. Some state-of-the-art methods are compared with the proposed method. Experimental results show that the proposed method improves reconstruction accuracy for a given number of measurements and more image details are recovered.

In this work we combing two novelty in the area of Analog Information Converter based on Compressed Sensing. A new architecture, the Spread Spectrum Random Modulation PreIntegration and a new design flow, the rakeness based design of a Compressed Sensing system. We demonstrate that combining these approaches produces a strong reduction of the internal chipping frequency in the sensing coupled with a high compression ratio with respect to standard Analog to Digital Converter.

Compressive sensing (CS) theory proves that signals can be recovered from far fewer random measurements than that suggested by Nyquist-Shannon theorem. This advantage of CS is extremely useful in some data processing applications, especially in video processing. Considering that nonlocal patch-based CS methodology has achieved an impressive performance in image CS field, it motivates us to generalize the idea into the video CS filed. In this paper, by taking account of the spatiotemporal information, we propose a novel strategy to reconstruct the non-key frames of a video sequence by introducing the nonlocal similar patch group from some key frames. Experimental results illustrate that our proposed nonlocal patch-based video CS recovery algorithm can extremely exploit the information of the key frames, and achieve a better performance compared with some existing popular video CS recovery algorithms. It is noted that our idea to construct a spatiotemporal comprehensive patch group may be combined with any nonlocal patch-based CS method for video reconstruction, which leads to various of nonlocal patch-based compressive sensing video recovery algorithms.