Yechao Bai

Compressed sensing image reconstruction via adaptive sparse nonlocal regularization

Compressed sensing (CS) has been successfully utilized by many computer vision applications. However,the task of signal reconstruction is still challenging, especially when we only have the CS measurements of an image (CS image reconstruction). Compared with the task of traditional image restoration (e.g., image denosing, debluring and inpainting, etc.), CS image reconstruction has partly structure or local features. It is difficult to build a dictionary for CS image reconstruction from itself. Few studies have shown promising reconstruction performance since most of the existing methods employed a fixed set of bases (e.g., wavelets, DCT, and gradient spaces) as the dictionary, which lack the adaptivity to fit image local structures. In this paper, we propose an adaptive sparse nonlocal regularization (ASNR) approach for CS image reconstruction. In ASNR, an effective self-adaptive learning dictionary is used to greatly reduce artifacts and the loss of fine details. The dictionary is compact and learned from the reconstructed image itself rather than natural image dataset. Furthermore, the image sparse nonlocal (or nonlocal self-similarity) priors are integrated into the regularization term, thus ASNR can effectively enhance the quality of the CS image reconstruction. To improve the computational efficiency of the ASNR, the split Bregman iteration based technique is also developed, which can exhibit better convergence performance than iterative shrinkage/thresholding method. Extensive experimental results demonstrate that the proposed ASNR method can effectively reconstruct fine structures and suppress visual artifacts, outperforming state-of-the-art performance in terms of both the PSNR and visual measurements.

Image denoising via group sparsity residual constraint

Group sparsity has shown great potential in various low-level vision tasks (e.g, image denoising, deblurring and inpainting). In this paper, we propose a new prior model for image denoising via group sparsity residual constraint (GSRC). To enhance the performance of group sparse-based image denoising, the concept of group sparsity residual is proposed, and thus, the problem of image denoising is translated into one that reduces the group sparsity residual. To reduce the residual, we first obtain some good estimation of the group sparse coefficients of the original image by the first-pass estimation of noisy image, and then centralize the group sparse coefficients of noisy image to the estimation. Experimental results have demonstrated that the proposed method not only outperforms many state-of-the-art denoising methods such as BM3D and WNNM, but results in a faster speed.

Performance analysis of MIMO beamforming with imperfect feedback

SUMMARY The quality of channel state information at the transmitter (CSIT) is critical to MIMO beamforming systems. However, in practical wireless systems, CSIT suffers from imperfections originating from quantization effects, feedback error and feedback delay. In this paper, we study the impact of feedback error and delay on the symbol error rate of MIMO beamforming systems with finite rate feedback. The feedback channel is modeled as a uniform symmetric channel. We derive an symbol error rate upper bound that is tight for a good beamformer. We also quantify the diversity gain and array gain loss due to the feedback error and delay. The codebook design method that is applicable to the beamforming systems with error or delay feedback is discussed. Both analytical and simulation results show that feedback error and delay will make the system behave badly at high signal-to-noise ratios. Copyright

Group Sparsity Residual Constraint for Image Denoising with External Nonlocal Self-Similarity Prior

Abstract Nonlocal image representation has been successfully used in many image-related inverse problems including denoising, deblurring and deblocking. However, most existing methods only consider the nonlocal self-similarity (NSS) prior of degraded observation image, and few methods use the NSS prior from natural images. In this paper we propose a novel method for image denoising via group sparsity residual constraint with external NSS prior (GSRC-ENSS). Different from the previous NSS prior-based denoising methods, two kinds of NSS prior (e.g., NSS priors of noisy image and natural images) are used for image denoising. In particular, to enhance the performance of image denoising, the group sparsity residual is proposed, and thus the problem of image denoising is translated into reducing the group sparsity residual. Because the groups contain a large amount of NSS information of natural images, to reduce the group sparsity residual, we obtain a good estimation of the group sparse coefficients of the original image by the external NSS prior based on Gaussian Mixture Model (GMM) learning, and the group sparse coefficients of noisy image are used to approximate the estimation. To combine these two NSS priors better, an effective iterative shrinkage algorithm is developed to solve the proposed GSRC-ENSS model. Experimental results demonstrate that the proposed GSRC-ENSS not only outperforms several state-of-the-art methods, but also delivers the best qualitative denoising results with finer details and less ringing artifacts.

A novel range alignment method for ISAR based on linear T/R array model

In inverse synthetic aperture radar (ISAR) imaging, translation compensation should be done before range-Doppler imaging process, and range alignment is the first step for translation compensation. In order to remove the limitation of integer range bin and align the echoes precisely for ISAR range alignment, combining with the advantage of array signal processing at fractional unit delay compensation, we propose a novel range alignment method based on linear transmitting/receiving (T/R) array. Firstly the ISAR imaging system is modeled as a linear T/R array. Then based on the snapshot imaging model of linear T/R array, range alignment is accomplished by wave path difference compensation which is transformed into the phase difference compensation in frequency domain between adjacent array elements. The phase difference compensation consists of integer range bin alignment and decimal time delay compensation which is implemented by the phase rotation’s estimation and compensation in frequency domain. Finally, the results of simulation data and real radar data are provided to demonstrate the effectiveness of the proposed method.

Resource allocation in multiple-relay systems exploiting opportunistic energy harvesting

Dear editor, Energy harvesting technique, as an important solution for green communication, has attracted significant concerns from industry and academia. In wireless relay systems, if relays (or destination) are (is) charged by wireless energy, new relaying policies need be developed to satisfy the requirements of information rate and energy transfer efficiency. Refs. [1, 2] investigated how to achieve various tradeoffs by optimizing the transmission schemes for simultaneous wireless information and power transfer in relay systems. Refs. [3,4] concentrated on the performance analysis of relay systems, in which relays harvested energy and forwarded information signals with time switching (TS) or power switching (PS) relaying protocol.

Efficient Data Fusion Using Random Matrix Theory

This letter addresses multisensor data fusion under the Gaussian noise. Under the Gauss–Markov model assumptions, data fusion based on maximum likelihood estimation (MLE) is the minimum variance unbiased estimator. Nonetheless, we propose a linear fusion algorithm based on the random matrix theory, which yields a biased estimator. The proposed estimator has a lower mean squared error (MSE) than the MLE fusion method when the dimensionality of signal is larger than the number of sensors, which is the typical use case in modern fusion systems. The fusion coefficients are directly solved in the proposed method without iteration, and this method can be considered as an approximate implementation of the linear minimum MSE (LMMSE) estimator. Numerical simulations demonstrate the performance gain of the proposed fusion method.

High-resolution inverse synthetic aperture radar imaging for large rotation angle targets based on segmented processing algorithm

Abstract. In inverse synthetic aperture radar (ISAR) imaging, the migration through resolution cells (MTRCs) will occur when the rotation angle of the moving target is large, thereby degrading image resolution. To solve this problem, an ISAR imaging method based on segmented preprocessing is proposed. In this method, the echoes of large rotating target are divided into several small segments, and every segment can generate a low-resolution image without MTRCs. Then, each low-resolution image is rotated back to the original position. After image registration and phase compensation, a high-resolution image can be obtained. Simulation and real experiments show that the proposed algorithm can deal with the radar system with different range and cross-range resolutions and significantly compensate the MTRCs.

Joint Information and Energy Transfer in Selection Relay Systems

In this paper, we consider a three-point relay system, in which the relay has no fixed energy supplies and thus needs to replenish energy from RF signals transmitted by the source via wireless energy transfer (WET). We propose optimal selection relaying scheme when the source knows partial channel state information (CSI). To maximize the ergodic throughput under the peak/total power constraints of source and energy causality constraint of relay, the joint optimization of power control and information/energy transfer scheduling is formulated as a non-convex optimization of functional. By introducing new variables and constraints into the problem, the problem is solved by combining the fractional programming, convex optimization and linear search. Our results provide useful guidelines for the efficient design of relay systems with relay powered by WET.

A fast algorithm for single source 3-D localization of MIMO radar with uniform circular transmit array

A low computation complexity algorithm for single source three dimensional (3-D) localization of multiple-input multiple-output (MIMO) radar with uniform circular transmit array is presented in this letter. The direction of departure (DOD) and the direction of arrival (DOA) are estimated respectively. Based on the geometry of MIMO radar, the 3-D coordinates of target are calculated. The proposed algorithm is free of eigen-value decomposition, thus has lower computational complexity. Moreover, it has no restriction in the number of elements for the uniform circular array (UCA). Simulation results demonstrate the effectiveness of the proposed algorithm.