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Trajectory prediction constitutes a key technology for intelligent systems to forecast future movements of dynamic agents, yet it faces significant challenges due to the uncertainty of motion behavior. We propose iDMa, a stochastic trajectory prediction framework that pioneers the integration of diffusion model with Mamba architecture to achieve high-precision and high-efficiency trajectory generation. Our approach introduces two key innovations: (1) a dual-parameter learning mechanism that optimizes noise estimation of mean and variance space, unlike conventional diffusion methods that employ fixed variance during the denoising process, so as to constrain the feasible domain more accurately; (2) a hybrid denoising backbone network that incorporates Transformer encoders and Mamba blocks. Compared to existing state-of-the-art methods, iDMa reduces the average displacement error (ADE) by 4.76% (0.20 vs. 0.21) on the ETH-UCY dataset and 1.85% (7.95 vs. 8.10) on the SDD dataset.

In underwater imaging, achieving high-quality imagery is essential but challenging due to factors such as wavelength-dependent absorption and complex lighting dynamics. This paper introduces MEvo-GAN, a novel methodology designed to address these challenges by combining generative adversarial networks with genetic algorithms. The key innovation lies in the integration of genetic algorithm principles with multi-scale generator and discriminator structures in Generative Adversarial Networks (GANs). This approach enhances image details and structural integrity while significantly improving training stability. This combination enables more effective exploration and optimization of the solution space, leading to reduced oscillation, mitigated mode collapse, and smoother convergence to high-quality generative outcomes. By analyzing various public datasets in a quantitative and qualitative manner, the results confirm the effectiveness of MEvo-GAN in improving the clarity, color fidelity, and detail accuracy of underwater images. The results of the experiments on the UIEB dataset are remarkable, with MEvo-GAN attaining a Peak Signal-to-Noise Ratio (PSNR) of 21.2758, Structural Similarity Index (SSIM) of 0.8662, and Underwater Color Image Quality Evaluation (UCIQE) of 0.6597.

In this paper, we propose an aerial images stitching method based on an as-projective-as-possible (APAP) algorithm, aiming at the problem artifacts, distortions, or stitching failure due to fewer feature points for multispectral aerial image with certain parallax. Our method incorporates accelerated nonlinear diffusion algorithm (AKAZE) into APAP algorithm. First, we use the fast and stable AKAZE to extract the feature points of aerial images, and then, based on the registration model of the APAP algorithm, we add line protection constraints, global similarity constraints, and local similarity constraints to protect the image structure information, to produce a panorama. Experimental results on several datasets demonstrate that proposed method is effective when dealing with multispectral aerial images. Our method can suppress artifacts, distortions, and reduce incomplete splicing. Compared with state-of-the-art image stitching methods, including APAP and adaptive as-natural-as-possible image stitching (AANAP), and two of the most popular UAV image stitching tools, Pix4D and OpenDroneMap (ODM), our method achieves them both quantitatively and qualitatively.

In the field of person re-identification, the extraction of pedestrian features is mainly focused on the extraction of features from the whole pedestrian or limb torso, and the facial features are less used. The facial features is integrated into the network to enhance pedestrian recognition accuracy rate. By introducing the MTCNN facial extraction network in the framework of person re-identification network Spindle Net, and improves the accuracy of person re-identification by improving the weight of facial features in the overall pedestrian characteristics. The experimental results show that the accuracy of Rank-1 on the CUHK01, CUHK03, VIPeR, PRID, i-LIDS, and 3DPeS data sets is 7% higher than that of Spindle Net. 目前在行人重识别 (person re-identification) 领域对行人特征的提取主要集中在整体行人或肢体躯干分别提取特征 较少使用面部特征。将面部特征融入到网络中以提高行人重识别的准确率。在行人重识别网络 Spindle Net 的框架中引入 MTCNN面部提取网络，通过提高面部特征在整体行人特征中的权重来提高行人重识别的准确率。实验结果表明，文中提出的网络相比于 Spindle Net 在CUHK01，CUHK03，VIPeR，PRID，i-LIDS，3DPeS数据集上Rank-1的准确率平均提升7%。

Infrared and visible image fusion is an effective method to solve the lack of single sensor imaging. The purpose is that the fusion images are suitable for human eyes and conducive to the next application and processing. In order to solve the problems of incomplete feature extraction, loss of details, and less samples of common data sets, it is not conducive to training, an end-to-end network architecture for image fusion is proposed. U-net is introduced into image fusion, and the final fusion result is obtained by using the generative adversarial network. Through its special convolution structure, the important feature information is extracted to the maximum extent, and the sample does not need to be cut to avoid the problem of reducing the fusion accuracy, but also to improve the training speed. Then the U-net extracted feature is confronted with the discriminator containing infrared image, and the generator model is obtained. The experimental results show that the present algorithm can obtain the fusion image with clear outline, prominent texture and obvious target. SD, SF, SSIM, AG and other indicators are obviously improved. 红外与可见光图像进行融合是解决单一传感器成像不足的有效手段，目的是得到适合人眼并有利于下一步应用和处理的融合图像。为解决大部分方法特征提取不全面，细节纹理丢失及公共数据集样本较少不利于训练等问题，提出一种用于图像融合的端到端网络结构。将U-net特有的卷积结构用于图像融合，最大程度地提取并保留源图像的重要特征信息。再通过生成对抗网络得到最后的融合结果，将U-net提取的特征输入生成器与包含红外图像的鉴别器进行对抗，得到训练模型。实验结果表示，所提算法能够得到轮廓清晰、纹理突出、目标明显的融合图像，SD、SF、SSIM、AG等指标明显得到提升。

High-dynamic range imaging technology is an effective method to improve the limitations of a camera’s dynamic range. However, most current high-dynamic imaging technologies are based on image fusion of multiple frames with different exposure levels. Such methods are prone to various phenomena, for example motion artifacts, detail loss and edge effects. In this paper, we combine a dual-channel camera that can output two different gain images simultaneously, a semi-supervised network structure based on an attention mechanism to fuse multiple gain images is proposed. The proposed network structure comprises encoding, fusion and decoding modules. First, the U-Net structure is employed in the encoding module to extract important detailed information in the source image to the maximum extent. Simultaneously, the SENet attention mechanism is employed in the encoding module to assign different weights to different feature channels and emphasis important features. Then, a feature map extracted from the encoding module is input to the decoding module for reconstruction after fusing by the fusion module to obtain a fused image. Experimental results indicate that the fused images obtained by the proposed method demonstrate clear details and high contrast. Compared with other methods, the proposed method improves fused image quality relative to several indicators.

In the field of person re-identification, the extraction of pedestrian features is mainly focused on the extraction of features from the whole pedestrian or limb torso, and the facial features are less used. The facial features is integrated into the network to enhance pedestrian recognition accuracy rate. By introducing the MTCNN facial extraction network in the framework of person re-identification network Spindle Net, and improves the accuracy of person re-identification by improving the weight of facial features in the overall pedestrian characteristics. The experimental results show that the accuracy of Rank-1 on the CUHK01, CUHK03, VIPeR, PRID, i-LIDS, and 3DPeS data sets is 7% higher than that of Spindle Net.

Infrared and visible image fusion is an effective method to solve the lack of single sensor imaging. The purpose is that the fusion images are suitable for human eyes and conducive to the next application and processing. In order to solve the problems of incomplete feature extraction, loss of details, and less samples of common data sets, it is not conducive to training, an end-to-end network architecture for image fusion is proposed. U-net is introduced into image fusion, and the final fusion result is obtained by using the generative adversarial network. Through its special convolution structure, the important feature information is extracted to the maximum extent, and the sample does not need to be cut to avoid the problem of reducing the fusion accuracy, but also to improve the training speed. Then the U-net extracted feature is confronted with the discriminator containing infrared image, and the generator model is obtained. The experimental results show that the present algorithm can obtain the fusion im

TG391; 红外与可见光图像进行融合是解决单一传感器成像不足的有效手段,目的是得到适合人眼并有利于下一步应用和处理的融合图像.为解决大部分方法特征提取不全面,细节纹理丢失及公共数据集样本较少不利于训练等问题,提出一种用于图像融合的端到端网络结构.将U-net特有的卷积结构用于图像融合,最大程度地提取并保留源图像的重要特征信息.再通过生成对抗网络得到最后的融合结果,将U-net提取的特征输入生成器与包含红外图像的鉴别器进行对抗,得到训练模型.实验结果表示,所提算法能够得到轮廓清晰、纹理突出、目标明显的融合图像,SD、SF、SSIM、AG等指标明显得到提升.

TP751.1; 目前在行人重识别(person re-identification)领域对行人特征的提取主要集中在整体行人或肢体躯干分别提取特征,较少使用面部特征.将面部特征融入到网络中以提高行人重识别的准确率.在行人重识别网络Spindle Net的框架中引入MTCNN面部提取网络,通过提高面部特征在整体行人特征中的权重来提高行人重识别的准确率.实验结果表明,文中提出的网络相比于Spindle Net在CUHK01,CUHK03,VIPeR,PRID,i-LIDS,3DPeS数据集上Rank-1的准确率平均提升7％.