When using digital cameras to capture video from a display screen, the occurrence of moiré patterns can lead to color distortions, significantly degrading the quality of both images and video. Given the escalating demand for video acquisition, designing algorithms for video demoiréing is a significant topic. In this paper, we introduce a novel attention-based network for this task, the spatiotemporal fusion transformer (STFT). By introducing temporal and spatial attention encoders and a multi-scale feature fusion method, STFT can learn dynamic spatial and temporal variations in moiré patterns. In the decoding phase, a self-attention mechanism is employed to learn temporal dependencies at both image-level and video-level, enhancing model moiré removal performance. Experimental results demonstrate a significant improvement in the performance of the proposed model over existing methods on public datasets. Furthermore, STFT can output visual attention maps for analyzing the distribution of moiré and the focus of model learning. STFT’s outstanding performance on the video rain removal task also demonstrates the robustness of our model, highlighting its potential for application to other restoration tasks.

Image demoiréing is a complex image-restoration task because of the color and shape variations of moiré patterns. With the development of mobile devices, mobile phones can now be used to capture images at multiple resolutions. This difficulty increases when attempting to remove moiré from both low- and high-resolution images, as different resolutions make it challenging for existing methods to match the scales and textures of moiré. To solve these problems, we built a mixed attention residual module (MARM) by combining multi-scale feature extraction and mixed attention methods. Based on MARM, we propose a multi-scale adaptive mixed attention network (MA2Net) that can adapt to input images of different sizes and remove moiré of various shapes. Our model achieved the best results on four public datasets with resolutions ranging from 256×256 to 4k. Extensive experiments demonstrated the effectiveness of our model, which outperformed state-of-the-art methods by a large margin. We also conducted experiments on image deraining to validate the effectiveness of our model in other image-restoration tasks, and MA2Net achieved state-of-the-art performance on the Rain200H dataset.

In computer numerical control machining, achieving high precision and speed requires the effective smoothing of tool paths defined by G01 codes. This study proposes an integrated time-parameter B-spline method that combines global smoothing techniques with geometric and kinematic approaches, significantly enhancing processing efficiency while adhering to both geometric and kinematic constraints. To optimize the kinematic properties of the B-spline curve, a non-dominated sorting genetic algorithm is employed to refine the knot vector, resulting in improved machining efficiency. Additionally, the incorporation of tangential velocity constraints leads to a robust trajectory planning algorithm that aligns with practical machining demands. Experimental results confirm the superiority of the proposed algorithm.