Boosting Convolutional Neural Networks with Middle Spectrum Grouped Convolution

This paper proposes a novel module called middle spectrum grouped convolution (MSGC) for efficient deep convolutional neural networks (DCNNs) with the mechanism of grouped convolution. It explores the broad "middle spectrum" area between channel pruning and conventional grouped convolution. Compared with channel pruning, MSGC can retain most of the information from the input feature maps due to the group mechanism; compared with grouped convolution, MSGC benefits from the learnability, the core of channel pruning, for constructing its group topology, leading to better channel division. The middle spectrum area is unfolded along four dimensions: group-wise, layer-wise, sample-wise, and attention-wise, making it possible to reveal more powerful and interpretable structures. As a result, the proposed module acts as a booster that can reduce the computational cost of the host backbones for general image recognition with even improved predictive accuracy. For example, in the experiments on ImageNet dataset for image classification, MSGC can reduce the multiply-accumulates (MACs) of ResNet-18 and ResNet-50 by half but still increase the Top-1 accuracy by more than 1%. With 35% reduction of MACs, MSGC can also increase the Top-1 accuracy of the MobileNetV2 backbone. Results on MS COCO dataset for object detection show similar observations. Our code and trained models are available at https://github.com/hellozhuo/msgc.Comment: 13 pages, 11 figures, submitted to IEEEE Transactions on xx

Sparse Aperture InISAR Imaging via Sequential Multiple Sparse Bayesian Learning

Interferometric inverse synthetic aperture radar (InISAR) imaging for sparse-aperture (SA) data is still a challenge, because the similarity and matched degree between ISAR images from different channels are destroyed by the SA data. To deal with this problem, this paper proposes a novel SA–InISAR imaging method, which jointly reconstructs 2-dimensional (2-D) ISAR images from different channels through multiple response sparse Bayesian learning (M-SBL), a modification of sparse Bayesian learning (SBL), to achieve sparse recovery for multiple measurement vectors (MMV). We note that M-SBL suffers a heavy computational burden because it involves large matrix inversion. A computationally efficient M-SBL is proposed, which, proceeding in a sequential manner to avoid the time-consuming large matrix inversion, is denoted as sequential multiple sparse Bayesian learning (SM-SBL). Thereafter, SM-SBL is introduced to InISAR imaging to simultaneously reconstruct the ISAR images from different channels. Numerous experimental results validate that the proposed SM-SBL-based InISAR imaging algorithm performs superiorly against the traditional single-channel sparse-signal recovery (SSR)-based InISAR imaging methods in terms of noise suppression, outlier reduction and 3-dimensional (3-D) geometry estimation

Bayesian high resolution range profile reconstruction of high-speed moving target from under-sampled data

Obtained by wide band radar system, high resolution range profile (HRRP) is the projection of scatterers of target to the radar line-of-sight (LOS). HRRP reconstruction is unavoidable for inverse synthetic aperture radar (ISAR) imaging, and of particular usage for target recognition, especially in cases that the ISAR image of target is not able to be achieved. For the high-speed moving target, however, its HRRP is stretched by the high order phase error. To obtain well-focused HRRP, the phase error induced by target velocity should be compensated, utilizing either measured or estimated target velocity. Noting in case of under-sampled data, the traditional velocity estimation and HRRP reconstruction algorithms become invalid, a novel HRRP reconstruction of high-speed target for under-sampled data is proposed. The Laplacian scale mixture (LSM) is used as the sparse prior of HRRP, and the variational Bayesian inference is utilized to derive its posterior, so as to reconstruct it with high resolution from the under-sampled data. Additionally, during the reconstruction of HRRP, the target velocity is estimated via joint constraint of entropy minimization and sparseness of HRRP to compensate the high order phase error brought by the target velocity to concentrate HRRP. Experimental results based on both simulated and measured data validate the effectiveness of the proposed Bayesian HRRP reconstruction algorithm.This work was supported in part by the National Natural Science Foundation of China under Grant 61801484 and Grant 61921001 and in part by the China Postdoctoral Science Foundation under Grant 2019TQ0072. The associate editor coordinating the review of this manuscript and approving it for publication was Prof. Abdesselam S. Bouzerdoum

A Novel Speed Compensation Method for ISAR Imaging with Low SNR

In this paper, two novel speed compensation algorithms for ISAR imaging under a low signal-to-noise ratio (SNR) condition have been proposed, which are based on the cubic phase function (CPF) and the integrated cubic phase function (ICPF), respectively. These two algorithms can estimate the speed of the target from the wideband radar echo directly, which breaks the limitation of speed measuring in a radar system. With the utilization of non-coherent accumulation, the ICPF-based speed compensation algorithm is robust to noise and can meet the requirement of speed compensation for ISAR imaging under a low SNR condition. Moreover, a fast searching implementation strategy, which consists of coarse search and precise search, has been introduced to decrease the computational burden of speed compensation based on CPF and ICPF. Experimental results based on radar data validate the effectiveness of the proposed algorithms

Logarithmic laplacian prior based bayesian inverse synthetic aperture radar imaging

This paper presents a novel Inverse Synthetic Aperture Radar Imaging (ISAR) algorithm based on a new sparse prior, known as the logarithmic Laplacian prior. The newly proposed logarithmic Laplacian prior has a narrower main lobe with higher tail values than the Laplacian prior, which helps to achieve performance improvement on sparse representation. The logarithmic Laplacian prior is used for ISAR imaging within the Bayesian framework to achieve better focused radar image. In the proposed method of ISAR imaging, the phase errors are jointly estimated based on the minimum entropy criterion to accomplish autofocusing. The maximum a posterior (MAP) estimation and the maximum likelihood estimation (MLE) are utilized to estimate the model parameters to avoid manually tuning process. Additionally, the fast Fourier Transform (FFT) and Hadamard product are used to minimize the required computational efficiency. Experimental results based on both simulated and measured data validate that the proposed algorithm outperforms the traditional sparse ISAR imaging algorithms in terms of resolution improvement and noise suppression.Published versio

Variational Bayesian Sparse Signal Recovery With LSM Prior

This paper presents a new sparse signal recovery algorithm using variational Bayesian inference based on the Laplace approximation. The sparse signal is modeled as the Laplacian scale mixture (LSM) prior. The Bayesian inference with the Laplacian models is a challenge because the Laplacian prior is not conjugate to the Gaussian likelihood. To solve this problem, we first introduce the inverse-gamma prior, which is conjugate to the Laplacian prior, to model the distinctive scaling parameters of the Laplacian priors. Then the posterior of the sparse signal, approximated by the Laplace approximation, is found to be Gaussian distributed with the expectation being the result of maximum a posterior (MAP) estimation. Finally the expectation-maximization (EM)-based variational Bayesian (VB) inference is utilized to accomplish the sparse signal recovery with the LSM prior. Since the proposed algorithm is a full Bayesian inference based on the MAP estimation, it achieves both the ability of avoiding structural error from the sparse Bayesian learning and the robustness to noise from the MAP estimation. Analysis on experimental results based on both simulated and measured data indicates that the proposed algorithm achieves the state-of-art performance in terms of sparse representation and de-noising.Published versio

Fast ISAR cross-range scaling using modified Newton method

This paper proposes a fast and novel cross-range scaling algorithm for inverse synthetic aperture radar (ISAR) imaging. The rotational motion of the target unavoidably results in high-order phase errors that blur the ISAR image. To achieve the cross-range scaling and compensate the quadratic phase error, the rotational velocity and rotational center of the target are jointly estimated by optimizing the ISAR image quality in terms of either entropy or contrast. Since it is a two-dimensional nonlinear optimization problem, the grid search is generally computationally inefficient and inaccurate. To improve the computational efficiency, a modified Newton method is introduced by adjusting the Hessian to be positively definite to ensure the iterative optimization process in a correct direction. The proposed algorithm offers the following desirable advantageous features. First, it automatically compensates the quadratic phase errors jointly with the scaling process to improve the image quality. Second, it is a data-driven, rather than image-driven, process that does not depend on the quality of ISAR image. It also performs satisfactorily for the sparse aperture data, while most other algorithms are invalid. The modified Newton method ensures fast convergence. For example, our numerical experiments achieve a precision of 10 -6 with less than ten iterations. Last but not least, the proposed algorithm is robust to noise because our experiments show that it is still effective when signal-to-noise ratio is as low as -10 dB

Logarithmic Laplacian Prior Based Bayesian Inverse Synthetic Aperture Radar Imaging

This paper presents a novel Inverse Synthetic Aperture Radar Imaging (ISAR) algorithm based on a new sparse prior, known as the logarithmic Laplacian prior. The newly proposed logarithmic Laplacian prior has a narrower main lobe with higher tail values than the Laplacian prior, which helps to achieve performance improvement on sparse representation. The logarithmic Laplacian prior is used for ISAR imaging within the Bayesian framework to achieve better focused radar image. In the proposed method of ISAR imaging, the phase errors are jointly estimated based on the minimum entropy criterion to accomplish autofocusing. The maximum a posterior (MAP) estimation and the maximum likelihood estimation (MLE) are utilized to estimate the model parameters to avoid manually tuning process. Additionally, the fast Fourier Transform (FFT) and Hadamard product are used to minimize the required computational efficiency. Experimental results based on both simulated and measured data validate that the proposed algorithm outperforms the traditional sparse ISAR imaging algorithms in terms of resolution improvement and noise suppression