Design and Preliminary Ground Experiment for Deployable Sunshade Structures of a Modular Space Telescope.

On-orbit assembling space telescope (OAST) is one of the most feasible methods to implement a large-scale space telescope. Unlike a monolithic space telescope (such as Hubble Space Telescope, HST) or a deployable space telescope (such as James Webb Space Telescope, JWST), OAST can be assembled in the spatial environment. To ensure proper telescope performance, OAST must be equipped with a large deployable sunshade. In order to verify the technology of the OAST, the authors propose a modular space telescope on the China Space Station (CSS) and design a deployable sunshade. The deployable mechanism of the sunshade is made up of a radial deployable mechanism and an axial deployable mechanism. The paper describes the overall design approach, the key component technologies, and the design and preliminary testing of a part of the deployable sunshade assembly.

Swin Transformer-Based Edge Guidance Network for RGB-D Salient Object Detection.

Salient object detection (SOD), which is used to identify the most distinctive object in a given scene, plays an important role in computer vision tasks. Most existing RGB-D SOD methods employ a CNN-based network as the backbone to extract features from RGB and depth images; however, the inherent locality of a CNN-based network limits the performance of CNN-based methods. To tackle this issue, we propose a novel Swin Transformer-based edge guidance network (SwinEGNet) for RGB-D SOD in which the Swin Transformer is employed as a powerful feature extractor to capture the global context. An edge-guided cross-modal interaction module is proposed to effectively enhance and fuse features. In particular, we employed the Swin Transformer as the backbone to extract features from RGB images and depth maps. Then, we introduced the edge extraction module (EEM) to extract edge features and the depth enhancement module (DEM) to enhance depth features. Additionally, a cross-modal interaction module (CIM) was used to integrate cross-modal features from global and local contexts. Finally, we employed a cascaded decoder to refine the prediction map in a coarse-to-fine manner. Extensive experiments demonstrated that our SwinEGNet achieved the best performance on the LFSD, NLPR, DES, and NJU2K datasets and achieved comparable performance on the STEREO dataset compared to 14 state-of-the-art methods. Our model achieved better performance compared to SwinNet, with 88.4% parameters and 77.2% FLOPs. Our code will be publicly available.

Global Guided Cross-Modal Cross-Scale Network for RGB-D Salient Object Detection.

RGB-D saliency detection aims to accurately localize salient regions using the complementary information of a depth map. Global contexts carried by the deep layer are key to salient objection detection, but they are diluted when transferred to shallower layers. Besides, depth maps may contain misleading information due to the depth sensors. To tackle these issues, in this paper, we propose a new cross-modal cross-scale network for RGB-D salient object detection, where the global context information provides global guidance to boost performance in complex scenarios. First, we introduce a global guided cross-modal and cross-scale module named G2CMCSM to realize global guided cross-modal cross-scale fusion. Then, we employ feature refinement modules for progressive refinement in a coarse-to-fine manner. In addition, we adopt a hybrid loss function to supervise the training of G2CMCSNet over different scales. With all these modules working together, G2CMCSNet effectively enhances both salient object details and salient object localization. Extensive experiments on challenging benchmark datasets demonstrate that our G2CMCSNet outperforms existing state-of-the-art methods.

Active alignment of space astronomical telescopes by matching arbitrary multi-field stellar image features.

Space-based optical astronomical telescopes are susceptible to mirror misalignments due to space disturbance in mechanics and temperature. Therefore, it is of great importance to actively align the telescope in orbit to continuously maintain imaging quality. Traditional active alignment methods usually need additional delicate wavefront sensors and complicated operations (such as instrument calibration and pointing adjustment). This paper proposes a novel active alignment approach by matching the geometrical features of several stellar images at arbitrary multiple field positions. Based on nodal aberration theory and Fourier optics, the relationship between stellar image intensity distribution and misalignments of the system can be modeled for an arbitrary field position. On this basis, an objective function is established by matching the geometrical features of the collected multi-field stellar images and modeled multi-field stellar images, and misalignments can then be solved through nonlinear optimization. Detailed simulations and a real experiment are performed to demonstrate the effectiveness and practicality of the proposed approach. This approach eliminates the need for delicate wavefront sensors and pointing adjustment, which greatly facilitates the maintainance of imaging quality.

Deep learning wavefront sensing for fine phasing of segmented mirrors.

Segmented primary mirror provides many crucial important advantages for the construction of extra-large space telescopes. The imaging quality of this class of telescope is susceptible to phasing error between primary mirror segments. Deep learning has been widely applied in the field of optical imaging and wavefront sensing, including phasing segmented mirrors. Compared to other image-based phasing techniques, such as phase retrieval and phase diversity, deep learning has the advantage of high efficiency and free of stagnation problem. However, at present deep learning methods are mainly applied to coarse phasing and used to estimate piston error between segments. In this paper, deep Bi-GRU neural work is introduced to fine phasing of segmented mirrors, which not only has a much simpler structure than CNN or LSTM network, but also can effectively solve the gradient vanishing problem in training due to long term dependencies. By incorporating phasing errors (piston and tip-tilt errors), some low-order aberrations as well as other practical considerations, Bi-GRU neural work can effectively be used for fine phasing of segmented mirrors. Simulations and real experiments are used to demonstrate the accuracy and effectiveness of the proposed methods.

Aberration compensation strategy for the radius of curvature error of the primary mirror in off-axis three-mirror anastigmatic telescopes.

This paper discusses compensation strategies for the aberration fields caused by the error in the radius of curvature (ROC) of the primary mirror (PM) in pupil-offset off-axis three-mirror anastigmatic (TMA) astronomical telescopes. Based on the nodal aberration theory framework, the specific astigmatic and coma aberrations of the off-axis three-mirror system in the presence of the ROC error of the PM are derived. It is demonstrated that some field-dependent aberration components can be induced by ROC error in the off-axis TMA telescopes, apart from the dominating field-constant aberration terms. To reduce the influence of the ROC error on the aberration fields, we propose two aberration compensation strategies: adjusting the position of the PM and introducing axial misalignment of the secondary mirror (SM). Through theoretical analysis and simulations, we conclude that the compensation strategy of changing the axial position of the PM can make the aberration distribution close to the nominal state; the compensation strategy of axially adjusting the SM can make the aberration distribution meet the observation requirements, which is more suitable for space applications. We also discuss compensating the effect of the ROC error using lateral misalignments.