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This paper introduces an innovative bilevel optimization approach to elevate the deblurring process. By integrating a weights variable nonlocal model with a spatially varying attached term, the methodology aims to achieve enhanced restoration outcomes. Theoretical scrutiny is dedicated to unraveling the solution of the model, paving the way for the development of an efficient algorithm meticulously crafted to compute the clean image. This algorithm excels in learning both the weights parameter and the balanced L 2 - L 1 attached parameter concurrently, thereby ensuring optimal performance. Through careful parameter selection, the proposed nonlocal deblurring model showcases superior effectiveness, surpassing existing models in terms of both performance and efficacy. [ABSTRACT FROM AUTHOR]

Abstract This paper deals with the problem of extracting information from non-stationary signals in the form of features that can be used for effective decision-making in both data analysis and machine learning for automatic classification systems. Suitable time–frequency (TF) and time–scale (TS) representations of such signals are reviewed for these purposes, and, the relationships between such TF and TS signal transformations is discussed. Both linear (atomic) decompositions and quadratic distributions are considered as well as other related methods such as fractional Fourier transform, polynomial Wigner–Ville distributions and time-varying order spectra. Current state-of-the-art methods are reviewed, and new results are presented including extensions of existing methods. Machine learning methodologies using TF/TS features can result in the design of systems that improve the classification of non-stationary signals. Using selected TF distributions (TFDs) and TS distributions (TSDs), the extraction of such TF/TS features is demonstrated on multi-channel recordings using channel fusion or feature fusion approaches. Extending the findings of previous studies, a TF/TS feature set is formed by including two complementary categories: signal related features and image features. The design of high-resolution TF/TS algorithms is then refined to account for issues of accuracy and robustness. Then, the desired TF/TS features are selected using different feature selection algorithms and compared with respect to the classification performance. Finally, other features from related methods are added, and comparisons performed. Improvements of up to 5% are obtained when using the chosen feature set after wrapper feature selection with channel feature fusion. [ABSTRACT FROM AUTHOR]

• A novel multi-step forecasting method of hourly PM 2.5 concentration is proposed. • Two boosting algorithms and two multi-step forecasting strategies are analyzed. • Error correction model is used to correct the prediction error. The multi-step forecasting of PM 2.5 concentration is helpful to realize the early warning of air pollution, but the accurate multi-step forecasting of PM 2.5 has certain difficulties. In this paper, a novel multi-step forecasting method of hourly PM 2.5 concentration is proposed. Two boosting algorithms, Modified AdaBoost.RT and Gradient Boosting, are used to enhance the extreme learning machine (ELM) for ensemble prediction of the PM 2.5. Then two multi-step forecasting strategies, multiple-input multiple-output (MIMO) and recursive, are used. Finally, through error correction model (ECM) the prediction error is corrected to obtain the hourly PM 2.5 multi-step forecasting results. Corresponding experiments are carried out through the PM 2.5 data sets of four cities, and the results show that: (1) the forecasting method proposed in this study can achieve a good multi-step forecasting effect of PM 2.5 , and changing the forecasting strategy or boosting algorithm has little influence on the forecasting effect; (2) the use of ECM can improve the PM 2.5 forecasting accuracy of the model, and as the forecasting steps increase, the improvement effect of ECM is more significant; (3) the forecasting framework proposed in this paper is effective, and the forecasting accuracy of the proposed method is significantly better than the corresponding single models and the existing models. [ABSTRACT FROM AUTHOR]

In this paper, a face hallucination method based on two-dimensional joint learning is presented. Unlike the existing works on face super-resolution algorithms that first reshape the image or image patch into 1D vector, in our study the spatial construction of the high resolution (HR) and the low resolution (LR) face image are efficiently maintained in the reconstruction procedure. Enlightened by the 1D joint learning approach for image super-resolution, we propose a 2D joint learning algorithm to map the original 2D LR and HR image patch spaces onto a unified feature subspace. Subsequently, the neighbor-embedding (NE) based super-resolution algorithm can be conducted on the unified feature subspace to estimate the reconstruction weights. With these weights, the initial HR facial image can be generated. To refine further the initial HR estimate, the global reconstruction constraint is exploited to improve the quality of reconstruction result. Experiments on the face databases and real-world face images demonstrate the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

In this paper, we proposed a novel online kernel-adaptive learning algorithm under the reproducing-kernel-Hilbert space (RKHS), which is called kernel q-Rényi (KqR) algorithm. The KqR algorithm is derived via constructing a q-Rényi kernel function as a cost function into kernel adaptive filtering algorithm (KAF). The vector-quantization (VQ) method is wildly used in the KAF algorithms, which is called quantized KAF. The quantized kernel q-Rényi (QKqR) algorithm is also proposed to significantly reduce computational cost via quantizing input space to curb the size of neural networks growth. The mean-square-convergence analysis of the QKqR algorithm is established, and the theoretical-value of the excess-mean-square-error (EMSE) are presented. The performance of the KqR and QKqR algorithms are investigated and demonstrated via tracking the Mackey-Glass (MG) time-series-prediction and the nonlinear channel equalization (NCE) under the non-Gaussian background interferences. [ABSTRACT FROM AUTHOR]