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It is widely known that the classic total variation(TV) model has been proven to be very effective in preserving sharp edges. However, the TV model suffers from the staircase effects which produce blocking artifacts in the restored images. In this paper, we propose a new hybrid regularization model by combining non-convex second order total variation with wavelet transform to restrain staircase effects and protect some details of the images. To compute the new model effectively, we propose an alternating minimization method for recovering images from the blurry and noisy observations. The new model is first transformed into several sub-problems, and the generalized iterated shrinkage algorithm, the Fourier transform method and projection method are used to solve these sub-problems, respectively. Numerical experiments show that the proposed model can restrain blocking artifacts while projecting sharp edges, and the restoration quality outperforms several state-of-the-art methods.

In order to predict the vessel traffic flow effectively, this paper used the non-convex low rank plus sparse decomposition model to decompose the traffic flow data into two parts with low rank matrix and sparse matrix. Then the framework used autoregressive integrated moving average (ARIMA) model to predict the low rank and sparse part respectively, and obtained the final result. Based on the data of vessel traffic flow in Tianjin port during 2003 to 2014. the results show that this model can reflect the seasonal variation of vessel traffic flow when compared with the gray system, neural network and combined forecasting model. It improves the prediction accuracy significantly and provides a new prediction method for vessel traffic flow. [ABSTRACT FROM AUTHOR]