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.

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.

The traditional temporal difference method under dynamic background will not extracted moving objects. In this paper, we propose a method based on positive and negative energy distribution and combine the LK optical flow method to achieve effective moving objects detection. Using series frames obtain the positive and negative energy distribution. The Gaussian kernel density function is used to estimate the density of the energy distribution histogram of the second frame. When the kernel density is smaller than the threshold value, it is considered to be the background movement with smaller energy. While the kernel density is larger than the threshold value, it is considered to be the moving objects with larger energy. Experimental results demonstrate that the method effectively detect the moving target in the dynamic background.