For vibration monitoring applications, the wide-bandwidth low noise MEMS accelerometer with high efficiency is required. Conventionally, the sensor resonant frequency is increased to achieve wide bandwidth, which however trades off the sensor sensitivity and degrades system noise and power efficiency (FoM). To meet this challenge, this paper proposed a Microlevers Assisted Hybrid Damping (MAHD) system. Instead of increasing the resonant frequency, the MAHD system employs critical hybrid damping for bandwidth improvement, in order to avoid degradation of the system noise and efficiency. The critical hybrid damping is implemented with an interface circuit providing tunable electrostatic damping force. Asensor structure with microlever is employed to enhance the merit of the critical damping system. The interface circuit is fabricated by a commercial$0.18\mu $m CMOS process and the sensor is fabricated by a commercial surface micromachining process. The measurement results show that, without increasing the sensor resonant frequency, the proposed method improve the system 3dB-bandwidth and 5%-bandwidth by 454% and 550%, respectively. The noise floor is$68.33\mu $g/$\surd $Hz with$600\mu $A current consumption.

For wearable applications, the small size low noise MEMS accelerometer with high efficiency is required. However, the sensitivity of the MEMS sensor reduces with the sensor size scaling down, leading to deterioration of the circuit noise. To meet this challenge, the interface circuit with high-voltage pulse excitation (HVPE) and active noise cancelation readout technique is proposed. The HVPE reduces the circuit noise and the wire resistance noise significantly without introducing additional electrostatic force to the sensor. The drift and mismatch problems of the HVPE are addressed by three specific correction circuits. The power efficiency of the system is optimized by the capacitance-to-voltage converter with active noise cancellation and pulse current supply. The propose HVPE technique is demonstrated in an interface IC fabricated by$0.18\mu $m BCD process and tested with a small size MEMS accelerometer (0.64mm2/axis). The measurement result shows that this IC has achieved a noise floor of$32.5\mu $g/$\surd $Hz with 2kHz bandwidth and$80\mu $W power consumption.

The total variation (TV) regularizer is a widely used technique in image processing tasks to model an image’s local smoothness property. Intrinsically, the TV regularizer imposes sparsity constraints on the gradient maps of the image, which inevitably weakens the image texture structure and thus affects the quality of image restoration. To alleviate this issue, we propose a novel texture-preserved total variation (TPTV) regularizer for hyperspectral image (HSI) by introducing a weighting scheme. Specifically, the weights are assigned to the gradient maps of HSI, which help slack the sparsity constraint for the pixels with large variations, thus preserving the texture structure. Additionally, we elaborate an empirical method to learn the weights adaptively from observed HSI. Then, we propose an HSI denoising method based on the TPTV regularizer. Experimental results on synthetic and real HSI illustrate the superiority of our proposed method over other state-of-the-art methods. In addition, the proposed weighting scheme can be finely embedded into other TV regularizers and protect the image texture. The experiment results also demonstrate that the denoising performance of the original method is significantly improved after embedding the weighting scheme.

Recently, deep learning techniques have been extensively studied for pansharpening, which aims to generate a high resolution multispectral (HRMS) image by fusing a low resolution multispectral (LRMS) image with a high resolution panchromatic (PAN) image. However, existing deep learning-based pansharpening methods directly learn the mapping from LRMS and PAN to HRMS. These network architectures always lack sufficient interpretability, which limits further performance improvements. To alleviate this issue, we propose a novel deep network for pansharpening by combining the model-based methodology with the deep learning method. Firstly, we build an observation model for pansharpening using the convolutional sparse coding (CSC) technique and design a proximal gradient algorithm to solve this model. Secondly, we unfold the iterative algorithm into a deep network, dubbed as Proximal PanNet, by learning the proximal operators using convolutional neural networks. Finally, all the learnable modules can be automatically learned in an end-to-end manner. Experimental results on some benchmark datasets show that our network performs better than other advanced methods both quantitatively and qualitatively.

Hyperspectral image (HSI) is always corrupted by various types of noise during image capturing, such as Gaussian noise, stripe noise, deadline noise, impulse noise, and more. Such complicated noise significantly degrades imaging quality and thus limits the performance of downstream vision tasks. Current HSI denoising methods tackle this problem by modeling either the spectral-spatial prior of HSI or the noise characteristic of HSI, and few work consider the two aspects simultaneously. In this paper, we propose a new HSI denoising method by simultaneously modeling the HSI prior and the HSI noise characteristic. Specifically, we firstly utilize the non independent and identically distributed (non i.i.d.) mixture of Gaussian (MoG) assumption to characterize the complex noise, which corresponds to optimize a weighted fidelity function. Secondly, we exploit HSI’s non-local similarity and spatial-spectral correlation priors by applying non-local low rank model. Thirdly, we design an adaptive edge preserving total variation regularization term to characterize the non-local smooth property of HSI. Finally, we propose a new denoising model and develop effective ADMM algorithm to solve it. Extensive experiments on simulated data and real data substantiate the superiority of the proposed method beyond state-of-the-arts.

Blind hyperspectral unmixing (HU), as a crucial technique for hyperspectral data exploitation, aims to decompose mixed pixels into a collection of constituent materials weighted by the corresponding fractional abundances. In recent years, nonnegative matrix factorization (NMF) based methods have become more and more popular for this task and achieved promising performance. Among these methods, two types of properties upon the abundances, namely the sparseness and the structural smoothness, have been explored and shown to be important for blind HU. However, all of previous methods ignores another important insightful property possessed by a natural hyperspectral images (HSI), non-local smoothness, which means that similar patches in a larger region of an HSI are sharing the similar smoothness structure. Based on previous attempts on other tasks, such a prior structure reflects intrinsic configurations underlying a HSI, and is thus expected to largely improve the performance of the investigated HU problem. In this paper, we firstly consider such prior in HSI by encoding it as the nonlocal total variation (NLTV) regularizer. Furthermore, by fully exploring the intrinsic structure of HSI, we generalize NLTV to non-local HSI TV (NLHTV) to make the model more suitable for the bind HU task. By incorporating these two regularizers, together with a non-convex log-sum form regularizer characterizing the sparseness of abundance maps, to the NMF model, we propose novel blind HU models named NLTV/NLHTV and log-sum regularized NMF (NLTV-LSRNMF/NLHTV-LSRNMF), respectively. To solve the proposed models, an efficient algorithm is designed based on alternative optimization strategy (AOS) and alternating direction method of multipliers (ADMM). Extensive experiments conducted on both simulated and real hyperspectral data sets substantiate the superiority of the proposed approach over other competing ones for blind HU task.

Stripe noise removal is a fundamental problem in remote sensing image processing. Many efforts have been made to resolve this problem. Recently, a state-of-the-art method was proposed from image-decomposition perspective. This method argued that the stripe and clear image can be simultaneously estimated by modeling the directional structure of stripes and the local smoothness of remote sensing images. However, the potential of this method cannot be fully delivered when confronting with dense stripes with high intensity. In this letter, we further consider the nonlocal self-similarity of image patches in the spatiospectral volume in terms of nonlocal total variation and propose a method of better robustness to dense stripes. Experimental results on both synthetic and real multispectral data show that the proposed method outperforms other competing methods in the remote sensing image destriping task.

Dynamic cerebral perfusion computed tomography (DCPCT) has the ability to evaluate the hemodynamic information throughout the brain. However, due to multiple 3-D image volume acquisitions protocol, DCPCT scanning imposes high radiation dose on the patients with growing concerns. To address this issue, in this paper, based on the robust principal component analysis (RPCA, or equivalently the low-rank and sparsity decomposition) model and the DCPCT imaging procedure, we propose a new DCPCT image reconstruction algorithm to improve low-dose DCPCT and perfusion maps quality via using a powerful measure, called Kronecker-basis-representation tensor sparsity regularization, for measuring low-rankness extent of a tensor. For simplicity, the first proposed model is termed tensor-based RPCA (T-RPCA). Specifically, the T-RPCA model views the DCPCT sequential images as a mixture of low-rank, sparse, and noise components to describe the maximum temporal coherence of spatial structure among phases in a tensor framework intrinsically. Moreover, the low-rank component corresponds to the "background" part with spatial-temporal correlations, e.g., static anatomical contribution, which is stationary over time about structure, and the sparse component represents the time-varying component with spatial-temporal continuity, e.g., dynamic perfusion enhanced information, which is approximately sparse over time. Furthermore, an improved nonlocal patch-based T-RPCA (NL-T-RPCA) model which describes the 3-D block groups of the "background" in a tensor is also proposed. The NL-T-RPCA model utilizes the intrinsic characteristics underlying the DCPCT images, i.e., nonlocal self-similarity and global correlation. Two efficient algorithms using alternating direction method of multipliers are developed to solve the proposed T-RPCA and NL-T-RPCA models, respectively. Extensive experiments with a digital brain perfusion phantom, preclinical monkey data, and clinical patient data clearly demonstrate that the two proposed models can achieve more gains than the existing popular algorithms in terms of both quantitative and visual quality evaluations from low-dose acquisitions, especially as low as 20 mAs.

In this paper, we propose a novel approach to hyperspectral image super-resolution by modeling the global spatial-and-spectral correlation and local smoothness properties over hyperspectral images. Specifically, we utilize the tensor nuclear norm and tensor folded-concave penalty functions to describe the global spatial-and-spectral correlation hidden in hyperspectral images, and 3D total variation (TV) to characterize the local spatial-and-spectral smoothness across all hyperspectral bands. Then, we develop an efficient algorithm for solving the resulting optimization problem by combing the local linear approximation (LLA) strategy and alternative direction method of multipliers (ADMM). Experimental results on one hyperspectral image dataset illustrate the merits of the proposed approach.

Background subtraction has been a fundamental and widely studied task in video analysis, with a wide range of applications in video surveillance, teleconferencing, and 3D modeling. Recently, motivated by compressive imaging, background subtraction from compressive measurements (BSCM) is becoming an active research task in video surveillance. In this paper, we propose a novel tensor-based robust principal component analysis (TenRPCA) approach for BSCM by decomposing video frames into backgrounds with spatial-temporal correlations and foregrounds with spatio-temporal continuity in a tensor framework. In this approach, we use 3D total variation to enhance the spatio-temporal continuity of foregrounds, and Tucker decomposition to model the spatio-temporal correlations of video background. Based on this idea, we design a basic tensor RPCA model over the video frames, dubbed as the holistic TenRPCA model. To characterize the correlations among the groups of similar 3D patches of video background, we further design a patch-group-based tensor RPCA model by joint tensor Tucker decompositions of 3D patch groups for modeling the video background. Efficient algorithms using the alternating direction method of multipliers are developed to solve the proposed models. Extensive experiments on simulated and real-world videos demonstrate the superiority of the proposed approaches over the existing state-of-the-art approaches.

In this paper, we address the problem of estimating and removing non-uniform motion blur from a single blurry image. We propose a deep learning approach to predicting the probabilistic distribution of motion blur at the patch level using a convolutional neural network (CNN). We further extend the candidate set of motion kernels predicted by the CNN using carefully designed image rotations. A Markov random field model is then used to infer a dense non-uniform motion blur field enforcing motion smoothness. Finally, motion blur is removed by a non-uniform deblurring model using patch-level image prior. Experimental evaluations show that our approach can effectively estimate and remove complex non-uniform motion blur that is not handled well by previous approaches.

In this paper, we propose a new sparsity regularizer for measuring the low-rank structure underneath a tensor. The proposed sparsity measure has a natural physical meaning which is intrinsically the size of the fundamental Kronecker basis to express the tensor. By embedding the sparsity measure into the tensor completion and tensor robust PCA frameworks, we formulate new models to enhance their capability in tensor recovery. Through introducing relaxation forms of the proposed sparsity measure, we also adopt the alternating direction method of multipliers (ADMM) for solving the proposed models. Experiments implemented on synthetic and multispectral image data sets substantiate the effectiveness of the proposed methods.