To solve the problem that sparse coding in text image restoration has the shortcomings of limited expression of dictionary atoms and high computation complexity, a novel text image restoration algorithm was proposed based on sparse coding and ridge regression. Firstly, patches were used to train the dictionary for sparse representation at training stage and the sampled image were clustered based on the Euclidean distances between the sampled image patches and the dictionary atoms. Then, the ridge regressors between low-quality text image patches and clear text image patches were constructed in local manifold space to achieve the local multi-linear expansion of dictionary atoms and fast calculation. At last, the clear text image patches were directly calculated at testing stage by searching for the most similar dictionary atoms with low-quality text image patches without calculating the sparse coding of low-quality text image patches. The experimental results show that compared with the existing sparse coding algorithm, the proposed algorithm has improved Peak Signal-to-Noise Ratio (PSNR) by 0.3 to 1.1 dB and reduced computing time at one or two orders of magnitude. Therefore, this method provides a good and fast solution for text image restoration.

Concerning the problem of the background interference during the salient object detection, a key salient object detection algorithm was proposed based on filtering integration in this paper. The proposed algorithm integrated the locally guided filtering with the improved DoG (Difference of Gaussia) filtering, and made the salient object more highlighted. Then, the key points set was determined by using the saliency map, and the result of saliency detection was got by adjustment factor, which was more suitable for human visual system. The experimental results show that the proposed algorithm is superior to existing significant detection methods. And it can restrain the background interference effectively, and have higher precision and better recall rate compared with other methods, such as Local Contrast (LC), Spectral Residual (SR), Histogram-based Contrast (HC), Region Contrast (RC) and Frequency-Tuned (FT).

Concerning the difficulty of data sparsity and new user problems in many collaborative recommendation algorithms, a new collaborative recommendation algorithm based on matrix factorization and user nearest neighbor was proposed. To guarantee the prediction accuracy of the new users, the user nearest neighbor model based on user data and profile information was used. Meanwhile, large data sets and the problem of matrix sparsity would significantly increase the time and space complexity. Therefore, matrix factorization was introduced to alleviate the effect of data problems and improve the prediction accuracy. The experimental results show that the new algorithm can improve the recommendation accuracy effectively, and solve the problems of data sparsity and new user.