Concerning the fact that common background subtraction methods ignore the spatio-temporal continuity of foreground and the disturbance of dynamic background to foreground extraction, an improved background subtraction model was proposed based on Tensor Robust Principal Component Analysis (TRPCA). The improved tensor nuclear norm was used to constrain the background, which enhanced the low rank of background and retained the spatial information of videos. Then the regularization constraint was performed to the foreground by 3D Total Variation (3D-TV), so as to consider the spatio-temporal continuity of object and effectively suppress the interference of dynamic background and target movement on the foreground extraction. Experimental results show that the proposed model can effectively separate the foreground and background of videos. Compared with High-order Robust Principal Component Analysis (HoRPCA), Tensor Robust Principal Component Analysis with Tensor Nuclear Norm (TRPCA-TNN) and Kronecker-Basis-Representation based Robust Principal Component Analysis (KBR-RPCA), the proposed algorithm has the F-measure values all optimal or sub-optimal. It can be seen that, the proposed model effectively improves the accuracy of foreground and background separation, and suppresses the interference of complex weather and target movement on foreground extraction.

In view of the fact that the low rank and sparse methods generally regard the foreground as abnormal pixels in the background, which makes the foreground detection precision decrease in the complex scene, a new foreground detection method combining weighted Schatten- p norm with 3D Total Variation (3D-TV) was proposed. Firstly, the observed data were divided into low rank background, moving foreground and dynamic disturbance. Then 3D total variation was used to constrain the moving foreground and strengthen the prior consideration of the spatio-temporal continuity of the foreground objects, effectively suppressing the random disturbance of the anomalous pixels in the discontinuous dynamic background. Finally, the low rank performance of video background was constrained by weighted Schatten- p norm to remove noise interference. The experimental results show that, compared with Robust Principal Component Analysis (RPCA), Higher-order RPCA (HoRPCA) and Tensor RPCA (TRPCA), the proposed model has the highest F-measure value, and the optimal or sub-optimal values of recall and precision. It can be concluded that the proposed model can better overcome the interference in complex scenes, such as dynamic background and severe weather, and its extraction accuracy as well as visual effect of moving objects is improved.

Focusing on the issue that remote sensing fusion image based on Contourlet transform has low spatial resolution, a remote sensing image fusion algorithm based on Modified Contourlet Transform (MCT) was proposed. Firstly, the multi-spectral image was decomposed into intensity component, hue component and saturation component by Intensity-Hue-Saturation (IHS) transform; secondly, Modified Contourlet decomposition was done between the intensity component and the panchromatic image after histogram matching to get low-pass subband coefficients and high-pass subbands coefficients; and then, the low-pass subband coefficients were fused by the averaging method, and the high-pass subbands coefficients were merged by Novel Sum-Modified-Laplacian (NSML). Finally, the fusion result was regarded as the intensity component of multi-spectral image, and remote sensing fusion image was obtained by inverse IHS transform. Compared with the algorithms based on Principal Components Analysis (PCA) and Shearlet, based on PCA and wavelet, based on NonSubsampled Contourlet Transform (NSCT), the average gradient that was used for evaluating image sharpness of the proposed method respectively increased by 7.3%, 6.9% and 3.9%. The experimental results show that, the proposed method enhances the frequency localization of Contourlet transform and the utilization of decomposition coefficients, and on the basis of keeping multi-spectral information, it improves the spatial resolution of remote sensing fusion image effectively.

To improve the accuracy of focusd regions in multifocus image fusion based on multiscale transform, a multifocus image fusion algorithm was proposed based on NonSubsampled Shearlet Transform (NSST) and focused regions detection. Firstly, the initial fused image was acquired by the fusion algorithm based on NSST. Secondly, the initial focusd regions were obtained through comparing the initial fused image and the source multifocus images. And then, the morphological opening and closing was used to correct the initial focusd regions. Finally, the fused image was acquired by the Improved Pulse Coupled Neural Network (IPCNN) in the corrected focusd regions. The experimental results show that, compared with the classic image fusion algorithms based on wavelet or Shearlet, and the current popular algorithms based on NSST and Pulse Coupled Neural Network (PCNN), objective evaluation criterions including Mutual Information (MI), spatial frequency and transferred edge information of the proposed method are improved obviously. The result illustrates that the proposed method can identify the focusd regions of source images more accurately and extract more sharpness information of source images to fusion image.

To deal with the single-image scale-up problem, a super-resolution reconstruction algorithm based on dictionary learning and non-local similarity was proposed. The difference images between the high-resolution images and results of using iterative back-projection image reconstruction were obtained, and then the high and corresponding low dictionaries could be co-generated by training difference image patches and the corresponding low-resolution image patches via using K-Singular Value Decomposition (K-SVD) algorithm which was combined with the idea that the high and low dictionaries could be co-trained for super-resolution reconstruction. In addition, a non-local similarity regularization constraint was introduced in the new algorithm to further improve the quality of the reconstructed images. The experimental results show that the proposed algorithm achieves better results than learning-based algorithms in terms of both visual perception and objective evaluation.