Blind image deblurring of natural images still remains a demanding task. The traditional methods, pre-processes the uniform and non-uniform images with a deblurring algorithm and employs a low-rank prior algorithm. Th...
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Blind image deblurring of natural images still remains a demanding task. The traditional methods, pre-processes the uniform and non-uniform images with a deblurring algorithm and employs a low-rank prior algorithm. The rich textures do not possess enough similar patches in the deblurring process and this loss results in noisy images. Also, computational efficiency gets compromised during the performance of the succeeding process. In this study, the authors propose a novel method called, linearly uncorrelated principal component and deep convolution (LUPC-DC) for deblurring natural images. The natural images are first de-correlated with which good similar patches are extracted to generate a low-rank matrix by linearly uncorrelated principal component (PC) extraction. Then, the deep convolutional neural network model jointly extracts good similar patches and deblurs the first PCs. Eventually, good similar patches in the last PCs are suppressed using Hard Thresholding for computational efficiency. Analysis of concurrence performance of the algorithm confirms the viability of this method theoretically. In addition, simulation results and performance evaluations of image quality metrics are provided to assess the effectiveness of the proposed method. Moreover, the proposed method provides improvement in the peak-signal-to-noise ratio rate, success rate and reduction in the computation time for image deblurring.
Situation assessment and search are two key problems in computer game research. In general, as the game progresses, the difficulty of evaluating the situation of the game is significantly reduced, and the accuracy of ...
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Situation assessment and search are two key problems in computer game research. In general, as the game progresses, the difficulty of evaluating the situation of the game is significantly reduced, and the accuracy of the evaluation is significantly increased. Based on the famous chess game, this article proposes and implements a new scheme that combines the Monte Carlo tree search algorithm, the Alpha-Beta algorithm and the model based on the deep convolution neuralnetwork (CNN) to solve the computer game problem. This article first proposes a deep convolutional neural network model based on dots and boxes, including deep value network and deep strategy network, focusing on situation assessment and strategy recommendation, respectively. Then, using the Monte Carlo Tree Search (MCTS) algorithm as a framework, deep value network integrated MCTS algorithm and deep strategy network integrated MCTS algorithm are proposed. In both integrated models, Alpha-Beta complete search is used to truncate the Monte Carlo simulation process and improve simulation efficiency. Through competition with human players, the results show that the two integrated algorithm game systems have reached much higher intelligence level than ordinary humans in solving the problem of dots and boxes.
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