Motor Imagery (MI) is essential in the Brain-Computer Interface (BCI). Given the multi-source nature of EEG instability across subjects/sessions and the privacy concerns when dealing with data from other institutions, the importance of multi-source-free domain adaptation (MSFDA) becomes evident in conducting subject-independent applications. However, current MI approaches are lack of MSFDA for considering different sources’ importance, which may lead to negative transfer. In this work, we propose a novel two-stage MSFDA framework, namely Privacy-preserving Attention-Weighted domain adaptation (PAW), which is the first to emphasize different source importance in EEG MI by identifying crucial features across subjects and sessions. A domain discriminator is designed to find robust features within sessions in the training phase. In the adaptation phase, an attention weighted module determines the source importance, and a balanced confident set policy mitigates noise and imbalance samples. PAW delivers excellent performance via empirical evaluations on several public datasets. In particular, its adaptation phase is applicable across MSFDA scenarios, highlighting its broad application values.

Multiplicative noise removal is a challenging image processing problem, and most existing methods are based on the maximum a posteriori formulation and the logarithmic transformation of multiplicative denoising problems into additive denoising problems. Sparse representations of images have shown to be efficient approaches for image recovery. Following this idea, in this paper, we propose to learn a dictionary from the logarithmic transformed image, and then to use it in a variational model built for noise removal. Extensive experimental results suggest that in terms of visual quality, peak signal-to-noise ratio, and mean absolute deviation error, the proposed algorithm outperforms state-of-the-art methods.

Multiplicative noise removal problems have attracted much attention in recent years. Unlike additive noise removal problems, the noise is multiplied to the orginal image, so almost all information of the original image may disappear in the observed image. The main aim of this paper is to propose and study a strictly convex objective function for multiplicative noise removal problems. We also incorporate the modified total variation regularization in the objective function to recover image edges. We develop an alternating minimization algorithm to find the minimizer of such an objective function efficiently and also show the convergence of the minimizing method. Our experimental results show that the quality of images denoised by the proposed method is quite good.

In this paper, we study the restoration of blurred images corrupted by impulse noise or mixed impulse plus Gaussian noises. In the proposed method, we use the modified total variation minimization scheme to regularize the deblurred image and fill in suitable values for noisy image pixels where these are detected by median-type filters. An alternating minimization algorithm is employed to solve the proposed total variation minimization problem. Our experimental results show the proposed algorithm is very efficient and the quality of restored images by the proposed method is competitive with those restored by the existing variational image restoration methods.

In this paper, we consider and study a total variation minimization model for color image restoration. In the proposed model, we use the color total variation minimization scheme to denoise the deblurred color image. An alternating minimization algorithm is employed to solve the proposed total variation minimization problem. We show the convergence of the alternating minimization algorithm and demonstrate that the algorithm is very efficient. Our experimental results show that the quality of restored color images by the proposed method are competitive with the other tested methods.