To overcome the disadvantages of the unclear results and time consuming in the sparse representation of image super-resolution reconstruction with single redundant dictionary, a single image super-resolution reconstruction method based on multi-dictionary learning and image patches mapping was proposed. In the framework of the traditional sparse representation, firstly the gradient structure information of local image patches was explored, and a large number of training image patches were clustered into several groups by their gradient angles, from those clustered patches the corresponding dictionary pairs were learned. And then the mapping function was computed from low resolution patch to high resolution patch in each clustered group via learned dictionary pairs with the idea of neighbor embedding. Finally the reconstruction process was reduced to a projection of each input patch into the high resolution space by multiplying with the corresponding precomputed mapping function, which improved the images quality with less running time. The experimental results show that the proposed method improves the visual quality significantly, and increases the PSNR (Peak Signal-to-Noise Ratio) at least 0.4 dB compared with the anchored neighborhood regression algorithm.

Focusing on the issue that different scale noise exists in denoising and smoothing of 3D point cloud data model, a bilateral filtering denoising algorithm for 3D point cloud based on noise classification was proposed. Firstly, the noise points were subdivided into the large-scale and the small-scale noise, and the large-scale noise was removed with statistical filtering and radius filtering. Secondly, the curvature of the 3D point cloud data was estimated, and the bilateral filter was improved to enhance the robustness and security. Finally, the small-scale noise was smoothed with the improved bilateral filter to achieve the smoothing and denoising of 3D point clouds. Compared with the algorithms simply based on bilateral filtering or Fleishman bilateral filtering, the smoothing average error index of 3D point cloud data model obtained by the proposed method respectively decreased by 50.53% and 21.67%. The experimental results show that the proposed algorithm increases the efficiency of calculation by scale subdivion of noise points, and avoids excessive smoothing and detail distortion, which can better maintain the geometric characteristics of the model.