Single-Image Super-Resolution Method for Rotating Synthetic Aperture System Using Masking Mechanism

• Published in: Remote sensing (Basel, Switzerland) Switzerland), 2024-05, Vol.16 (9), p.1508
• Main Authors: Sun, Yu, Zhi, Xiyang, Jiang, Shikai, Shi, Tianjun, Song, Jiachun, Yang, Jiawei, Wang, Shengao, Zhang, Wei
• Format: Article
• Language: English
• Subjects: Aperture; Asymmetry; Comparative analysis; Digital imaging; Digital simulation; Fourier transforms; Geometrical optics; Geosynchronous orbits; Image enhancement; Image processing; Image quality; Image resolution; masked autoencoder; Methods; optical remote sensing; Optics; Primary mirrors; rectangular pupil; Remote sensing; rotating synthetic aperture; Rotation; Satellites; Space applications; super-resolution (SR); Symmetry; Synthetic apertures; vision transformer
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs16091508

The emerging technology of rotating synthetic aperture (RSA) presents a promising solution for the development of lightweight, large-aperture, and high-resolution optical remote sensing systems in geostationary orbit. However, the rectangular shape of the primary mirror and the distinctive imaging mechanism involving the continuous rotation of the mirror lead to a pronounced decline in image resolution along the shorter side of the rectangle compared to the longer side. The resolution also exhibits periodic time-varying characteristics. To address these limitations and enhance image quality, we begin by analyzing the imaging mechanism of the RSA system. Subsequently, we propose a single-image super-resolution method that utilizes a rotated varied-size window attention mechanism instead of full attention, based on the Vision Transformer architecture. We employ a two-stage training methodology for the network, where we pre-train it on images masked with stripe-shaped masks along the shorter side of the rectangular pupil. Following that, we fine-tune the network using unmasked images. Through the strip-wise mask sampling strategy, this two-stage training approach effectively circumvents the interference of lower confidence (clarity) information and outperforms training the network from scratch using the unmasked degraded images. Our digital simulation and semi-physical imaging experiments demonstrate that the proposed method achieves satisfactory performance. This work establishes a valuable reference for future space applications of the RSA system.