When dealing with incoming Unmanned Aerial Vehicles （UAVs）， it is crucial to recognize the formation of enemy UAVs quickly and accurately， in order to analyze and judge enemies’ combat intentions and formulate effective countermeasures. Therefore， a UAV swarm formation recognition algorithm based on multi-scale complex networks was proposed. Firstly， an adaptive threshold method was established to construct multi-scale complex networks using the UAV swarm formation， and the combination of eigenvalues corresponding to the adjacency matrices of these complex networks was selected to form a shape signature. Then， by introducing Hellinger distance to measure the difference between the shape signature of the formation to be recognized and the standard formation， so as to obtain the recognition results. Simulation results show that compared with the algorithm of obtaining multi-scale complex networks with hard thresholds， the proposed algorithm has better adaptability and robustness， has a higher recognition rate even when the target information is heavily corrupted， and has fewer parameters and lower time complexity.

Aiming at the balance optimization problem of Lightweight Convolutional Neural Network （LCNN） in accuracy and complexity， an adaptive multi-scale feature channel grouping optimization algorithm based on fast Non-dominated Sorting Genetic Algorithm （NSGA-Ⅱ） was proposed to optimize the feature channel grouping structure of LCNN. Firstly， the complexity minimization and accuracy maximization of the feature fusion layer structure in LCNN were regarded as two optimization objectives， and the dual-objective function modeling and theoretical analysis were carried out. Then， a LCNN structure optimization framework based on NSGA-Ⅱ was designed， and an adaptive grouping layer based on NSGA-Ⅱ was added to deep convolution layer in original LCNN structure， thus constructing an Adaptive Multi-scale Feature Fusion Network based on NSGA2 （NSGA2-AMFFNetwork）. Experimental results on image classification datasets show that compared with the manually designed network structure M_blockNet_v1， NSGA2-AMFFNetwork has the average accuracy improved by 1.220 2 percentage points， and the running time decreased by 41.07%. This above indicates that the proposed optimization algorithm can balance the complexity and accuracy of LCNN， and also provide more options for network structure with balanced performance for ordinary users who lack domain knowledge.

Aiming at the problems of blurred target boundaries and low image segmentation efficiency in the image segmentation process of ancient murals, a multi-class image segmentation model fused with a lightweight convolutional neural network named MC-DM (Multi-Class DeepLabV3+MobileNetV2 (Mobile Networks Vision 2)) was proposed. In the model, DeepLabV3+ architecture and MobileNetV2 network were combined together, and the unique spatial pyramid structure of DeepLabV3+ was utilized to perform multi-scale fusion of the convolutional features of the mural to reduce the loss of image details during the mural segmentation. First of all, the features of the input image were extracted by MobileNetV2 to ensure the accurate extraction of image information and reduce the time consumption at the same time. Secondly, the image features were processed through the dilated convolution, so that the receptive field was expanded, and more semantic information was obtained without changing the number of parameters. Finally, the bilinear interpolation method was utilized to up-sample the output feature image to obtain a pixel-level prediction segmentation map, so that the accuracy of image segmentation was ensured to the greatest extent. In the JetBrains PyCharm Community Edition 2019 environment, a dataset made of 1 000 mural scanning pictures was used for testing. Experimental results showed that the MC-DM model had a 1% improvement in training accuracy compared with the traditional SegNet (Segment Network)-based image segmentation model, and had a 2% improvement in accuracy compared with the image segmentation model based on PSPNet (Pyramid Scene Parsing Network), and the Peak Signal-to-Noise Ratio (PSNR) of the MC-DM model was 3 to 8 dB higher than those of the experimental comparison models on average, which verified the effectiveness of the model in the field of mural segmentation. The proposed model provides a new idea for the segmentation of ancient mural images.

Concerning the problem that the performance analysis method of Luby Transform (LT) codes under Gaussian elimination decoding algorithm is complicated and inaccurate, a novel performance analysis method based on probability transfer function was proposed. Firstly, for two LT codes with simple uniform degree distribution, the precise performance was studied and its quantitative expression was given. Secondly, the general LT code was investigated, and a simple but effective qualitative analysis method was proposed. Finally, the simulation work was done to verify the new method. In the comparison experiments with the traditional method which only gives the upper and the lower bounds of the rank of generated matrix, the maximum error of performance analysis results for simple uniform degree LT codes reduces to 0.0124, and the complexity of general LT codes decrease to O( k ²). Theoretical analysis shows that the proposed method can effectively guide the optimization design of LT codes in communication area.

To solve the problem of H.264 video copyright protection, a new blind watermarking algorithm was proposed. Based on the texture features of the picture, watermarking information was embedded on the Discrete Cosine Transform (DCT) domain of Instantaneous Decoding Refresh (IDR) frames. First, a rectangular sliding window was used to search the region of complex textures. Second, in the selected region, a 4×4 sub-block of maximum energy was chosen for embedding one watermarking bit. Last, one Alternating Current (AC) coefficient value of the selected 4×4 sub-block was modified adaptively. The experimental results show that Peak-Signal-to Noise Ratio (PSNR) decreases 0.15dB and the bitrate rises 0.49% on average, and the accuracy of watermark detection is above 91%; moreover, the algorithm can effectively resist the re-coded attacks of different Quantization Parameter (QP).

Considering that the traditional method of histogram equalization can make the image produce unnatural enhanced results, a new color image contrast enhancement approach was proposed, which equalized color components by dependently using co-occurrence matrix. Firstly, gray correlative characteristics of pixels were combined in 3×3 neighborhoods to construct a co-occurrence matrix. Secondly, the method of co-occurrence histogram equalization was applied to luminance component only and the chrominance components were preserved. Finally, dark channel prior was used to solve the problem mentioned above to obtain the ideal image. Comparing the proposed enhanced algorithm with the other three typical enhancement algorithms, it is shown that the proposed algorithm not only considers the whole and local image information, but also deals with the histogram spikes and halo effect. The experimental results demonstrate that the proposed method can enhance the color images effectively.

To improve the security and reliability of image transmission, an image encryption algorithm based on combination Low Density Parity Check (LDPC) coding with chaotic system was proposed. Firstly, the algorithm used parity encoding to extend pixels' value of image into 10 bits and calculated its deviation acted as chaotic initial value. Secondly, Arnold transformation was used to scramble the positions of image pixels and Henon mapping was used to diffuse the values of pixels. Finally, the high 2 bits were separated from 10 bits of pixel value and transmitted faultlessly by LDPC code, the other 8 bits acted as the encryption result. The experimental results show that the proposed algorithm has strong sensitivity to the keys and plaintext, possesses favorable avalanche effect, and it can resist plaintext attack and differential attack effectively. Moreover, the encryption result has strong ability of resisting cutting and noise attacks.