Self-adaptive Dictionary Research Articles

Application of Compressed Sensing Based on Adaptive Dynamic Mode Decomposition in Signal Transmission and Fault Extraction of Bearing Signal

Bearings are widely used in mechanical equipment; nevertheless, potential dangers are also widespread, making bearing fault detection very important. For large equipment, the amount of collected signals tends to be huge, which challenges both signal transmission and storage. To solve this problem, compressed sensing (CS), based on specific dynamic modes of adaptive truncated rank dynamic mode decomposition (ADMD), is adopted to achieve the purpose of compressing and transmitting the signal, as well as extracting fault features. Firstly, this paper has proposed a new fitness function, which is called the synthetic envelope kurtosis characteristic energy difference ratio, and adopted the improved particle swarm optimization algorithm (IPSO) to select the best truncated rank adaptively. Then, the historical signal attempts to be decomposed into a series of temporal and spatial coherent modes, through ADMD, and those modes are filtered and cascaded into a highly self-adaptive dictionary, the DMD dictionary, which approximates the original signal with some error. Next, CS is employed to compress and reconstruct the signal, in order to reduce storage space and improve transmission efficiency. Finally, signals of high quality can be reconstructed through orthogonal matching pursuit (OMP) algorithm. Compared with traditional dictionaries, the DMD dictionary, based on the mode structure generated by ADMD decomposition, ass proposed in this paper, can better represent the original signal in the simulation signal and have good noise reduction performance. The correlation coefficient (CORR) between the reconstructed signal and noise signal is 0.8109, between the reconstructed signal and non-noise signal is 0.9278, and the root mean square error (RMSE) is 0.0659 and 0.0351, respectively. Compared with the traditional SVD and EMD denoising methods, ADMD-CS has better noise reduction performance. In this paper, the signal-to-noise ratio (SNR) is taken as the quantitative indicator of denoising performance. It is found that the SNR of simulation signal and experimental signal processed by ADMD-CS is higher than that of the traditional denoising methods, which is 0.3017 and 0.8407, respectively. The storage space of the signal is quite smaller than traditional methods, and the compression ratios (CR) of the simulation and experimental signals are 66.16% and 59.08%, respectively. In conclusion, ADMD-CS has a good application prospect in signal transmission, storage, and feature extraction.

Realizing the Effective Detection of Tumor in Magnetic Resonance Imaging using Cluster-Sparse Assisted Super-Resolution

Recently, significant research has been done in Super-Resolution (SR) methods for augmenting the spatial resolution of the Magnetic Resonance (MR) images, which aids the physician in improved disease diagnoses. Single SR methods have drawbacks; they fail to capture self-similarity in non-local patches and are not robust to noise. To exploit the non-local self-similarity and intrinsic sparsity in MR images, this paper proposes the use of Cluster-Sparse Assisted Super-Resolution. This SR method effectively captures similarity in non-locally positioned patches by training on clusters of patches using a self-adaptive dictionary. This method of training also leads to better edge and texture detection. Experiments show that using Cluster-Sparse Assisted Super-Resolution for brain MR images results in enhanced detection of lesions leading to better diagnosis.

Image Compressive Sensing via Hybrid Nonlocal Sparsity Regularization.

This paper focuses on image compressive sensing (CS). As the intrinsic properties of natural images, nonlocal self-similarity and sparse representation have been widely used in various image processing tasks. Most existing image CS methods apply either self-adaptive dictionary (e.g., principle component analysis (PCA) dictionary and singular value decomposition (SVD) dictionary) or fixed dictionary (e.g., discrete cosine transform (DCT), discrete wavelet transform (DWT), and Curvelet) as the sparse basis, while single dictionary could not fully explore the sparsity of images. In this paper, a Hybrid NonLocal Sparsity Regularization (HNLSR) is developed and applied to image compressive sensing. The proposed HNLSR measures nonlocal sparsity in 2D and 3D transform domain simultaneously, and both self-adaptive singular value decomposition (SVD) dictionary and fixed 3D transform are utilized. We use an efficient alternating minimization method to solve the optimization problem. Experimental results demonstrate that the proposed method outperforms existing methods in both objective evaluation and visual quality.

Material decomposition for simulated dual-energy breast computed tomography via hybrid optimization method.

Dual-energy breast CT reconstruction has a potential application that includes separation of microcalcification from healthy breast tissue for assisting early breast cancer detection. To investigate and validate the noise suppression algorithm applied in the decomposition of the simulated breast phantom into microcalcification and healthy breast. The proposed hybrid optimization method (HOM) uses a simultaneous algebraic reconstruction technique (SART) output as a prior image, which is then incorporated into the self-adaptive dictionary learning. This self-adaptive dictionary learning seeks each group of patches to faithfully represent the learned dictionary, and the sparsity and non-local similarity of group patches are used to enforce the image regularization term of the prior image. We simulate a numerical phantom by adding different levels of Gaussian noise to test performance of the proposed method. The mean value of peak signal-to-noise ratio (PSNR), structural similarity (SSIM), and root mean square error (RMSE) for the proposed method are (49.043±1.571), (0.997±0.002), (0.003±0.001) and (51.329±1.998), (0.998±0.002), (0.003±0.001) for 35 kVp and 49 kVp, respectively. The PSNR of the proposed method shows greater improvement over TWIST (5.2%), SART (34.6%), FBP (40.4%) and TWIST (3.7%), SART (39.9%), FBP (50.3%) for 35 kVp and 49 kVp energy images, respectively. For the proposed method, the signal-to-noise ratio (SNR) of decomposed normal breast tissue (NBT) is (22.036±1.535), which exceeded that of TWIST, SART, and FBP by 7.5%, 49.6%, and 96.4%, respectively. The results reveal that the proposed algorithm achieves the best performance in both reconstructed and decomposed images under different levels of noise and the performance is due to the high sparsity and good denoising ability of minimization exploited to solve the convex optimization problem. This study demonstrates the potential of applying dual-energy reconstruction in breast CT to detect and separate clustered MCs from healthy breast tissues without noise amplification. Compared to other competing methods, the proposed algorithm achieves the best noise suppression performance for both reconstructed and decomposed images.

A novel sparse feature extraction method based on sparse signal via dual-channel self-adaptive TQWT

Sparse signal is a kind of sparsematrices which can carry fault information and simplify the signal at the same time. This can effectively reduce the cost of signal storage, improve the efficiency of data transmission, and ultimately save the cost of equipment fault diagnosis in the aviation field. At present, the existing sparse decomposition methods generally extract sparse fault characteristics signals based on orthogonal basis atoms, which limits the adaptability of sparse decomposition. In this paper, a self-adaptive atom is extracted by the improved dual-channel tunable Q-factor wavelet transform (TQWT) method to construct a self-adaptive complete dictionary. Finally, the sparse signal is obtained by the orthogonal matching pursuit (OMP) algorithm. The atoms obtained by this method are more flexible, and are no longer constrained to an orthogonal basis to reflect the oscillation characteristics of signals. Therefore, the sparse signal can better extract the fault characteristics. The simulation and experimental results show that the self-adaptive dictionary with the atom extracted from the dual-channel TQWT has a stronger decomposition freedom and signal matching ability than orthogonal basis dictionaries, such as discrete cosine transform (DCT), discrete Hartley transform (DHT) and discrete wavelet transform (DWT). In addition, the sparse signal extracted by the self-adaptive complete dictionary can reflect the time-domain characteristics of the vibration signals, and can more accurately extract the bearing fault feature frequency.

Joint group and residual sparse coding for image compressive sensing

Nonlocal self-similarity and group sparsity have been widely utilized in image compressive sensing (CS). Most existing methods utilize either internal priors or external priors. However, when the sampling rate is low, the internal prior information of degraded images is not enough for accurate restoration, resulting in loss of image edges and details. And external prior, which learned from clean data, may be not adaptive to the images. In this paper, we propose a joint group and residual sparse coding method for CS image recovery (JGRSC-CS). In the proposed JGRSC-CS, patch group is treated as the basic unit of sparse coding. Different from the idea of encoding a group with a single dictionary (e.g. DCT, Curvelet or K-SVD dictionary), two dictionaries (namely internal and external dictionaries) are applied to exploit the sparse representation of each group simultaneously. The internal self-adaptive dictionary is used to remove artifacts, and an external Gaussian Mixture Model (GMM) dictionary, learned from clean training images, is used to enhance details and texture. To make the proposed method effective and robust, the split Bregman method is adopted to reconstruct the whole image. Experimental results manifest that with acceptable computational complexity and good convergence property, the proposed JGRSC-CS algorithm outperforms existing state-of-the-art methods in both peak signal to noise ratio (PSNR) and visual quality.

Local segmentation of images using an improved fuzzy C-means clustering algorithm based on self-adaptive dictionary learning

Image segmentation is an active research topic in image processing. The Fuzzy C-means (FCM) clustering analysis has been widely used in image segmentation. As there is a large amount of delicate tissues such as blood vessels and nerves in medical images, noise generated during imaging process can easily affect successful segmentation of these tissues. The traditional FCM algorithm is not ideal for segmentation of images containing strong noise. In this study, we proposed an improved FCM algorithm with anti-noise capability. We first discussed the algorithm of dictionary learning for noise reduction. Then we developed a new image segmentation algorithm as a combination of the dictionary learning for noise reduction and the improved fuzzy C-means clustering. Lastly we used the algorithm of the improved FCM to segment images, during which we removed the non-target areas making use of the grayscale features of images and extracted accurately the areas of interests. The algorithm was tested using synthetic Shepp-Logan images and real medical magnetic resonance imaging (MRI) and computed tomography (CT) images. Compared to the synthetic data and real medical images segmented by the fuzzy C-means (FCM) clustering algorithm, the Kernel Fuzzy C-mean (KFCM) clustering algorithm, spectral clustering algorithm, the sparse learning based fuzzy C-means (SL_FCM) clustering algorithm, and the modified spatial KFCM (MSFCM) algorithm, the images segmented by the dictionary learning Fuzzy C-mean clustering (DLFCM) algorithm have higher partition coefficient, lower partition entropy, better visual perception, better clustering accuracy, and clustering purity.

Self-Similarity Sparse Representation for Image Restoration

Image restoration aims to restore an image from a degraded image. The degradation may occur during image acquisition or image transmission. Image degradation lowers the quality of the image. In this paper additive Gaussian noise is considered for degrading the original image. For restoring the image from degraded image the proposed method used both local and non-local similarity patterns. The restoration problem is modeled with regression model. Two regularization terms are considered for representing prior image information. One regularization term is for local patterns and other is for non-local similarity patterns. The additive local regularization term is used to restore the edges. The non-local regularization term works best for local smoothness and edge information will be lost. The proposed algorithm took a clean image of size 256x256 and added with Gaussian noise with different levels of noise levels. A self-adaptive dictionary is trained for a particular window of image with local and non-local patterns and stacked to three dimensional matrix. The patch size considered for training the dictionary is 10x10. For restoring each patch it searches best atoms form the trained dictionary. The efficiency of the algorithm is estimated by parameters mean square error, root mean square error, PSNR and FSIM. The algorithm is also tested for different images like cameraman, house, Barbara, Lena and parrot. The proposed algorithm is tested with conventional algorithms. .

A Novel Video Coding Framework Using a Self-Adaptive Dictionary

In this paper, we propose to use a self-adaptive redundant dictionary, consisting of all possible inter and intra prediction candidates, to directly represent the frame blocks in a video sequence. The self-adaptive dictionary generalizes the conventional predictive coding approach by allowing adaptive linear combinations of prediction candidates, which is solved by an rate-distortion aware L0-norm minimization problem using orthogonal least squares (OLS). To overcome the inefficiency in quantizing and coding coefficients corresponding to correlated chosen atoms, we orthonormalize the chosen atoms recursively as part of OLS process. We further propose a two-stage video coding framework, in which a second stage codes the residual from the chosen atoms using a modified discrete cosine transform (DCT) dictionary that is adaptively orthonormalized with respect to the subspace spanned by the first stage atoms. To determine the transition from the first stage to the second stage, we propose a rate-distortion (RD) aware adaptive switching algorithm. The proposed framework is further extended to accommodate variable block sizes ( $16\times 16$ , $8\times 8$ , and $4\times 4$ ), and the partition mode is derived by a fast partition mode decision algorithm. A context-adaptive binary arithmetic entropy coder is designed to code the symbols of the proposed coding framework. The proposed coder shows competitive, and in some cases better RD performance, compared with the HEVC video coding standard for P-frames.

The research of image inpainting algorithm using self-adaptive group structure and sparse representation

Focused on the issue that the object structure discontinuity and poor texture detail occurred in image inpainting method, the image inpainting algorithm based on self-adaptive group structure has proposed in this paper. The conception of self-adaptive group structure is different from traditional image patching operation and fixed group structure, which refers to the fact that a patch on the structure has fewer similar patches than the one within the textured region. A self-adaptive dictionary as well as the sparse representation model was established in the domain of self-adaptive group. Finally, the target cost function was solved by Split Bregman Iterational operation. The experimental results on target removing with Criminisi’s algorithm, GSR’s algorithm and SALSA’s algorithm in image pixels losting of image inpainting had shown that the proposed algorithm has better performance than other algorithms.

High-Speed Hyperspectral Video Acquisition By Combining Nyquist and Compressive Sampling.

We propose a novel hybrid imaging system to acquire 4D high-speed hyperspectral (HSHS) videos with high spatial and spectral resolution. The proposed system consists of two branches: one branch performs Nyquist sampling in the temporal dimension while integrating the whole spectrum, resulting in a high-frame-rate panchromatic video; the other branch performs compressive sampling in the spectral dimension with longer exposures, resulting in a low-frame-rate hyperspectral video. Owing to the high light throughput and complementary sampling, these two branches jointly provide reliable measurements for recovering the underlying HSHS video. Moreover, the panchromatic video can be used to learn an over-complete 3D dictionary to represent each band-wise video sparsely, thanks to the inherent structural similarity in the spectral dimension. Based on the joint measurements and the self-adaptive dictionary, we further propose a simultaneous spectral sparse (3S) model to reinforce the structural similarity across different bands and develop an efficient computational reconstruction algorithm to recover the HSHS video. Both simulation and hardware experiments validate the effectiveness of the proposed approach. To the best of our knowledge, this is the first time that hyperspectral videos can be acquired at a frame rate up to 100fps with commodity optical elements and under ordinary indoor illumination.

Simultaneous Depth and Spectral Imaging With a Cross-Modal Stereo System

This letter presents a novel approach for simultaneous depth and spectral imaging with a cross-modal stereo system. Two images of the target scene are captured at the same time: one compressively sampled hyperspectral measurement and one panchromatic measurement. The underlying hyperspectral cube is first reconstructed by leveraging the compressive sensing theory, during which a self-adaptive dictionary is learned from the panchromatic measurement to facilitate the reconstruction. The depth information of the scene is then recovered by estimating a disparity map between the hyperspectral cube and the panchromatic measurement through stereo matching. This disparity map, once obtained, is used to align the hyperspectral and panchromatic measurements to boost the hyperspectral reconstruction in an iterative manner. Through hardware experiments, for the first time to our knowledge, we demonstrate a snapshot system that allows for simultaneous depth and spectral imaging. The proposed system is capable of recording depth and spectral videos of dynamic scenes.

Group-based sparse representation for image restoration.

Traditional patch-based sparse representation modeling of natural images usually suffer from two problems. First, it has to solve a large-scale optimization problem with high computational complexity in dictionary learning. Second, each patch is considered independently in dictionary learning and sparse coding, which ignores the relationship among patches, resulting in inaccurate sparse coding coefficients. In this paper, instead of using patch as the basic unit of sparse representation, we exploit the concept of group as the basic unit of sparse representation, which is composed of nonlocal patches with similar structures, and establish a novel sparse representation modeling of natural images, called group-based sparse representation (GSR). The proposed GSR is able to sparsely represent natural images in the domain of group, which enforces the intrinsic local sparsity and nonlocal self-similarity of images simultaneously in a unified framework. In addition, an effective self-adaptive dictionary learning method for each group with low complexity is designed, rather than dictionary learning from natural images. To make GSR tractable and robust, a split Bregman-based technique is developed to solve the proposed GSR-driven ℓ0 minimization problem for image restoration efficiently. Extensive experiments on image inpainting, image deblurring and image compressive sensing recovery manifest that the proposed GSR modeling outperforms many current state-of-the-art schemes in both peak signal-to-noise ratio and visual perception.