At present, super-resolution reconstruction of a single image achieves a good effect, but most models achieve the good effect by increasing the number of network layers rather than exploring the correlation between channels. In order to solve this problem, an image super-resolution reconstruction method based on Channel Attention mechanism (CA) and Depthwise Separable Convolution (DSC) was proposed. The multi-path global and local residual learning were adopted by the entire model. Firstly, the shallow feature extraction block was used to extract the features of the input image. Then, the channel attention mechanism was introduced in the deep feature extraction block, and the correlation of the channels was increased by adjusting the weights of the feature graphs of different channels to extract the high-frequency feature information. Finally, a high-resolution image was reconstructed. In order to reduce the huge parameter influence brought by the attention mechanism, the depthwise separable convolution technology was used in the local residual block to greatly reduce the training parameters. Meanwhile, the Adaptive moment estimation (Adam) optimizer was used to accelerate the convergence of the model, so as to improve the algorithm performance. The image reconstruction by the proposed method was carried out on Set5 and Set14 datasets. Experimental results show that the images reconstructed by the proposed method have higher Peak Signal-to-Noise Ratio (PSNR) and Structural SIMilarity index (SSIM), and the parameters of the proposed model are reduced to 1/26 of that of the depth Residual Channel Attention Network (RCAN) model.

Integer-interval means the set of the left and right endpoints of the interval (which are integers) and all integers between them. The positional relationship between integer-intervals is the relation between the positions of two integer-intervals. Aiming at the positional relationship between integer-intervals, a secure two-party computation problem was proposed, in other words, a private-preserving determination problem of integer-interval positional relationship was proposed. In this problem, two users with private-preserving integer-intervals were helped to correctly determine the positional relationship between the two integer-intervals of them with the private preserved. Six positional relationships between two integer-intervals were defined, the 0-1 coding scheme of integer-intervals was given, and a determination rule for integer-interval positional relationship was proved. Then, based on the Goldwasser-Micali cryptosystem and semi-honest attacker model, a secure two-party computation protocol for solving the private-preserving determination problem of integer-interval positional relationship was designed. The protocol was proved to be both correct and secure, and the performance of the protocol was analyzed and explained.

The determination of the relationship between integer point and integer interval in the sense of privacy preserving is an important secure multi-party computation problem, but there are some defects in the existing solutions, such as low efficiency, privacy disclosure, and even possible wrong determination. Aiming at these defects, an improved secure two-party computation protocol for solving this determination problem was constructed. Firstly, analysis of the existing protocols was given and some shortcomings of the protocols were pointed out. Secondly, a new 0-1 coding rule for integer point and integer interval was defined, based on this, a necessary and sufficient condition for an integer point belonging to an integer interval was proved. Finally, by using the necessary and sufficient condition as the determination standard, a secure two-party computation protocol for determining wether the integer point belonging to the integer interval was proposed based on the Goldwasser-Micali encryption system, and its correctness and the security under the semi-honest model were proved. Analysis shows that compared with the existing solutions, the proposed protocol has better privacy preserving feature and will not output wrong results, in addition, both the computation complexity and the communication complexity of the protocol are reduced by about half while the round complexity remains the same.

In order to improve the recognition ability for online behavior continuous sequences and enhance the stability of behavior recognition model, a novel online behavior recognition method based on Probabilistic Latent-Dynamic Conditional Random Field (PLDCRF) from surveillance video was proposed. Firstly, the Space-Time Interest Point (STIP) was used to extract behavior features. Then, the PLDCRF model was applied to identify the activity state of indoor human body. The proposed PLDCRF model incorporates the hidden state variables and can construct the substructure of gesture sequences. It can select the dynamic features of gesture and mark the unsegmented sequences directly. At the same time, it can also mark the conversion process between behaviors correctly to improve the effect of behavior recognition greatly. Compared with Hidden Conditional Random Field (HCRF), Latent-Dynamic Conditional Random Field (LDCRF) and Latent-Dynamic Conditional Neural Field (LDCNF), the recognition rate comparison results of 10 different behaviors show that, the proposed PLDCRF model has a stronger recognition ability for continuous behavior sequences and better stability.

Since the soft shadow achieved by the existing shadow generation algorithms of Augmented Reality (AR) is unrealistic, the authors proposed a shadow generation algorithm using adaptive sampling and background fusion. First, the authors computed shadow spatial location distribution of virtual objects by using planar shadow algorithm which took occlusion into account. Then, to improve the procedure of soft shadow generation in swell and erode algorithm, an adaptive sampling method which got illuminant union according to shape types was presented. Finally, since shadow color gotten by gray image method was limited to single channel, the authors presented a method based on multi-channel and background fusion. The experimental results show that in the proposed algorithm the color of soft shadow is more reasonable and the method of soft shadow rendering is more effective. Consequently, the presented algorithm improves the realism of soft shadow.