Ultrasound imaging is one of the most important diagnostic techniques of modern medical imaging. However, due to the presence of multiplicative speckle noise, the development of ultrasound imaging has been limited. For this problem, an improved strategy for Bayesian Non-Local Means (NLM) filtering algorithm was proposed. Firstly,a Bayesian formulation was applied to derive an NLM filter adapted to a relevant ultrasound noise model, which leads to two methods of calculating distance between the image blocks, the Pearson distance and the root distance. Secondly, to lighten the computational burden, a image block pre-selection process was used to accelerate the algorithm when a similar image block was selected in the non-local area. In addition, the relationship between parameter and noise variance was determined experimentally, which made the parameter being adaptive to the noise.Finally, the VS (Visual Studio) and OpenCV (Open source Computer Visual library) were used to realize the algorithm, making the program running time greatly reduced. In order to evaluate the denoising performance of the proposed algorithm, experiments were conducted on both phantom images and real ultrasound images. The experimental results show that the algorithm has a great improvement in the performance of removing speckle noise and achieves satisfactory results in terms of preserving the edges and image details, compared with some existing classical algorithms.

For the problem of denoising images corrupted by mixed noise such as Additive White Gaussian Noise (AWGN) with Salt-and-Pepper Impulse Noise (SPIN) and Random-Valued Impulse Noise (RVIN), an improved image restoration algorithm on the basis of the existing weighted encoding method was proposed. The image priors about sparse representation and non-local similarity were integrated. Firstly, the sparse representation based on the dictionary was used to build a variational denoising model and a weighting factor was designed for data fidelity term to suppress impulse noise. Secondly, the method of non-local means was used to get an initialized denoised image and then a mask matrix was built to remove impulse noise points to get the good non-local similarity prior knowledge. Finally, the image sparsity prior and non-local similarity prior were integrated into the regularization of the variational model. The final denoised image was obtained by solving the variational model. The experimental results show that in different noise ratios, the Peak Signal-to-Noise Ratio (PSNR) of the proposed algorithm increased 1.7 dB than that of fuzzy weighted non-local means filter, and the Feature Similarity Index (FSIM) increased 0.06. Compared with weighted encoding method, the PSNR increased 0.64 dB, and the FSIM increased 0.03. The proposed method has better recovery performance especially for the texture strong images and can retain real information of the image.

Top-k dominating query algorithm requires high consumption of time and space to build combined indexes on the attributes, and the query accuracy is low for the data with same attribute values. To solve these problems, a Ranking-k algorithm was given in this paper. The proposed Ranking-k algorithm is a new subspace dominating query algorithm combining the B⁺-trees with probability distribution model. Firstly, the ordered lists for each data attribute were constructed by the B⁺-trees. Secondly, the round-robin scheduling algorithm was used to scan ordered attribute lists satisfying skyline criterion. Some candidate tuples were generated and k end tuples were obtained. Thirdly, the dominated scores of end tuples were calculated by using the probability distribution model according to the generated candidate tuples and end tuples. Through iterating the above process, the optimal query results were obtained. The experimental results show that the overall query efficiency of the proposed Ranking-k algorithm is improved by 94.43% compared with the Basic-Scan Algorithm (BSA) and by 7.63% compared with the Differential Algorithm (DA), and the query results of Ranking-k algorithm are much closer to theoretical values in comparison of the Top-k Dominating with Early Pruning (TDEP) algorithm, BSA and DA.