A semiblind image deconvolution algorithm with spatially adaptive total variation (SATV) regularization is introduced. The spatial information in different image regions is incorporated into regularization by using the edge indicator called difference eigenvalue to distinguish flat areas from edges. Meanwhile, the split Bregman method is used to optimize the proposed SATV model. The proposed algorithm integrates the spatial constraint and parametric blur‐kernel and thus effectively reduces the noise in flat regions and preserves the edge information. Comparative results on simulated images and real passive millimeter‐wave (PMMW) images are reported.

The study of electroencephalogram (EEG) signals is crucial for understanding brain function and has extensive applications in clinical diagnosis, neuroscience, and brain–computer interface technology. This paper addresses the challenge of recognizing motor imagery EEG signals with few channels, which is essential for portable and real-time applications. A novel framework is proposed that applies a continuous wavelet transform to convert time-domain EEG signals into two-dimensional time-frequency representations. These images are then concatenated into channel-dependent multilayer EEG time-frequency representations (CDML-EEG-TFR), incorporating multidimensional information of time, frequency, and channels, allowing for a more comprehensive and enriched brain representation under the constraint of few channels. By adopting a deep convolutional neural network with EfficientNet as the backbone and utilizing pre-trained weights from natural image datasets for transfer learning, the framework can simultaneously learn temporal, spatial, and channel features embedded in the CDML-EEG-TFR. Moreover, the transfer learning strategy effectively addresses the issue of data sparsity in the context of a few channels. Our approach enhances the classification accuracy of motor imagery EEG signals in few-channel scenarios. Experimental results on the BCI Competition IV 2b dataset show a significant improvement in classification accuracy, reaching 80.21%. This study highlights the potential of CDML-EEG-TFR and the EfficientNet-based transfer learning strategy in few-channel EEG signal classification, laying a foundation for practical applications and further research in medical and sports fields.

The clutter becomes non-stationary for a space-based surveillance radar (SBSR), which is harmful for the moving targets detection due to the earth’s rotation. The non-stationarity will degrade the accuracy of clutter covariance matrix (CCM) estimation and increase the clutter degree of freedom (DOF), thereby degrading the performance of clutter suppression. To solve this problem, this paper proposes a novel non-stationary clutter suppression method using an elevation oblique subspace projection method. After analyzing the range ambiguity and non-stationarity of the clutter, the proposed method utilized the oblique projection matrix to project the signal onto the subspace spanned by the near-range and far-range clutter components along the subspace spanned by the main lobe clutter component. Then, the projected signal was used to estimate the elevation covariance matrix and calculate the optimal weight vector for the elevation adaptive filter. The proposed method can suppress the non-stationary clutter effectively with a higher improvement factor (IF) and a narrower main lobe width. Finally, the simulation results were given to verify the correctness and effectiveness of the proposed method.

For a single-channel spaceborne synthetic aperture radar (SAR), the usage of a low pulse repetition frequency (PRF) is an effective technical way to extend the range swath. The sub-aperture imaging strategy is usually used to solve the problem of azimuth spectrum aliasing under the condition of a low PRF. However, the required up-sampling processing before the coherent synthesis of sub-images will lead to spectrum discontinuity between adjacent sub-images, which leads to an obvious grating lobe phenomenon after the process of sub-image synthesis, resulting in a significant decrease in image quality. For this issue, a high-resolution wide-swath (HRWS) imaging algorithm for a spaceborne SAR with a low PRF is proposed in this paper based on optimal spectrum shift processing. First, each sub-aperture is imaged using the typical range migration algorithm (RMA), and then all sub-images are up-sampled at the same time. Then, based on the criterion of the minimum grating lobe, the optimal spectrum shift is estimated. Finally, the spectrum of all sub-images is shifted and then all the shifted sub-images are synthesized coherently. The simulation data processing results verify the effectiveness of the proposed method.