Xinggan Zhang

Analyzing the group sparsity based on the rank minimization methods

Sparse coding has achieved a great success in various image processing studies. However, there is not any benchmark to measure the sparsity of image patch/group because sparse discriminant conditions cannot keep unchanged. This paper analyzes the sparsity of group based on the strategy of the rank minimization. Firstly, an adaptive dictionary for each group is designed. Then, we prove that group-based sparse coding is equivalent to the rank minimization problem, and thus the sparse coefficients of each group are measured by estimating the singular values of each group. Based on that measurement, the weighted Schatten p-norm minimization (WSNM) has been found to be the closest solution to the real singular values of each group. Thus, WSNM can be equivalently transformed into a non-convex ℓp-norm minimization problem in group-based sparse coding. Experimental results on two applications: image in painting and image compressive sensing (CS) recovery show that the proposed scheme outperforms many state-of-the-art methods.

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.

Joint Data and Energy Transmission in a Two-Hop Network With Multiple Relays

Wireless energy transfer (WET) is a promising technique to prolong the lifetime of an energy-constrained wireless network. In this paper, we consider a two-hop communication system with multiple relay nodes powered by WET. To minimize the transmission time, we propose an optimal transmission scheme, in which the relay nodes adopt a “harvest-then-decode and forward” policy. The optimal transmission time allocation of the source and each relay node is calculated by a linear program under causality constraints on both data and energy arrivals. The analytical and simulation results reveal the relationship between the optimal time allocation and system parameters.

Wireless Information and Energy Transfer in Fading Relay Channels

Wireless energy transfer is a promising solution to provide convenient and steady energy supplies for low-power relays. This paper investigates the simultaneous information and energy transfer in fading relay channels, where the relay has no fixed energy supply and replenishes energy from radio frequency signals transmitted by the source. Assume that the relay can switch among energy harvesting, information decoding, and information retransmission in each channel fading state. Our objective is to maximize the ergodic throughput by optimizing the mode switching rule and transmit power jointly under the data and energy causality constraints. When the source knows channel state information (CSI) of all links, to make the problem tractable, for the relay, we neglect the causality constraints during the transmission, and only consider the total data and energy constraints. We thus obtain an upper bound on the ergodic throughput by solving a convex optimization problem. Numerical results show that the achievable rate is very close to the upper bound when we apply the optimized parameters to a practical system. When the source only knows CSI of partial links, the whole transmission process is divided into two phases: the source transmits in the first phase and the relay decodes and forwards received bits using the harvested energy in the second phase. The throughput maximization problem is solved by combing convex optimization, fractional programming, and linear search. We also consider the simplified network topology when a direct link between the source and destination is unavailable. In this network, we propose algorithms based on bisection method to obtain the optimal parameters in information/energy transfer scheduling and power control when the source knows full or partial CSI. The simulation results reveal that the throughput gain brought by wireless powered relaying in different system configurations when the source knows full or partial CSI. Moreover, the effect of the relay position is discussed.

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.

Group-based sparse representation for image compressive sensing reconstruction with non-convex regularization

Patch-based sparse representation modeling has shown great potential in image compressive sensing (CS) reconstruction. However, this model usually suffers from some limits, such as dictionary learning with great computational complexity, neglecting the relationship among similar patches. In this paper, a group-based sparse representation method with non-convex regularization (GSR-NCR) for image CS reconstruction is proposed. In GSR-NCR, the local sparsity and nonlocal self-similarity of images is simultaneously considered in a unified framework. Different from the previous methods based on sparsity-promoting convex regularization, we extend the non-convex weighted Lp (0 < p < 1) penalty function on group sparse coefficients of the data matrix, rather than conventional L1-based regularization. To reduce the computational complexity, instead of learning the dictionary with a high computational complexity from natural images, we learn the principle component analysis (PCA) based dictionary for each group. Moreover, to make the proposed scheme tractable and robust, we have developed an efficient iterative shrinkage/thresholding algorithm to solve the non-convex optimization problem. Experimental results demonstrate that the proposed method outperforms many state-of-the-art techniques for image CS reconstruction.