Speckle noise is a significant challenge in synthetic aperture radar (SAR) images, severely degrading the visual quality and compromising subsequent image interpretation tasks. While existing despeckling methods can reduce noise, they often fail to strike a appropriate balance between noise suppression and the preservation of fine image details. To address this issue, in this paper, we propose a novel SAR image despeckling method that leverages both structural image priors and noise distribution characteristics in an end-to-end framework. Our approach consists of two key components: a dual-branch subnet for coarse despeckling and noise estimation, and a noise-guided Transformer-based subnet for final image refinement. The dual-branch subnet decouples the tasks of noise estimation and despeckling, improving both noise suppression accuracy and structural detail preservation. Furthermore, a combination of grouped pooling attention (GPA) and context-aware fusion (CAF) modules enables effective multi-scale feature fusion by jointly capturing local details and global contextual information. The noise estimation branch generates adaptive priors that guide the Transformer refinement, which incorporates deformable convolutions and a masked self-attention mechanism to selectively focus on relevant image regions. Extensive experiments conducted on both synthetic and real SAR datasets demonstrate that the proposed method consistently outperforms current state-of-the-art methods, achieving superior speckle suppression while preserving fine details more effectively.

This study centered around the practical problem that there is no machine available for deep planting with large holes in hilly and mountainous areas of China. According to the principle of spiral lifting, a conical, double-spiral hole-forming machine was innovatively designed. The structural design and parameter calculation were completed. By discrete-element-method (DEM) simulation, the optimal lead and rotation speed of the hole former were obtained, and the hole-forming mechanisms of soil cutting, soil lifting, soil discharging, soil extruding, and soil returning were further revealed. The field test results indicated that the prototype had the advantages of convenient operation and good performance, and the formed holes met the agronomic requirements, with a qualification rate of 88.5%. In addition, it was found that the soil moisture content has a great influence on the formation of holes. Under the condition of low moisture content, the residence time at the bottom of a hole should be appropriately increased to improve the qualification rate of the holes formed. Our research results provided theoretical guidance and technical support for the design, optimization, popularization, and application of a hole-forming machine for deep planting with large holes (DPLH).

Teleoperation is essential for manipulation in hazardous or inaccessible environments, but is often limited by reduced perception, limited control flexibility, and low operator immersion. To overcome these issues, we present a mixed-reality (MR)–assisted teleoperation system for mobile robotic arms. The system integrates stereoscopic vision via a head-mounted display and an augmented-reality (AR) interface providing real-time feedback and safety alerts. A multimodal control strategy further supports complex maneuvers. In the experiments, participants achieved a 92.42% success rate in object retrieval and placement, and success rates in narrow corridor tasks were 84.08% with conventional displays and 89.41% under MR-based collaborative control. These findings suggest potential usability benefits for the remote operation of mobile robotic arms.