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Your search keyword '"Zhi, Xiyang"' showing total 145 results
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145 results on '"Zhi, Xiyang"'

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

Author

Sun, Yu, Zhi, Xiyang, Jiang, Shikai, Shi, Tianjun, Song, Jiachun, Yang, Jiawei, Wang, Shengao, and Zhang, Wei

Subjects
*SYNTHETIC apertures, *REMOTE sensing, *TRANSFORMER models, *TECHNOLOGICAL innovations, *SYNTHETIC aperture radar, *OPTICAL remote sensing, *DIGITAL computer simulation, *MIRROR images
Abstract

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. [ABSTRACT FROM AUTHOR]

Published
2024
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28. Self-Supervised Denoising via Blind Feature Extraction and Diffusion-Based Texture Generation

Author

Bao, Guangzhen, Zhi, Xiyang, Zhang, Pengfei, Hu, Jianming, Shi, Tianjun, Jiang, Shikai, Wu, Yayun, and Gong, Jinnan

Abstract

In the field of remote sensing, detection in dimly lit or shadowed areas has traditionally been difficult because of detector noise. Given that noise in real-world images of remote sensing exhibits spatial correlation, existing self-supervised methods encounter difficulties in reconciling the suppression of spatially correlated noise with the preservation of local texture details. To address this challenge, we propose a self-supervised model that combines blind-spot feature extraction with diffusion-based texture generation to fine denoising of real-world images under adverse conditions. We first introduce a blind-spot feature extraction structure based on the fusion of U-Net with blind-spot net (UBSN) and blind transformer (BTF). In UBSN, we integrate multistride blind-spot convolution (BSC) + dilated convolution (DC) feature extraction nodes and employ a Reshuffle strategy in skip-layer connections to maintain large-scale blind-spot characteristics. Additionally, we design a transformer structure for blind spot between patches to remove the noise with spatial correlations while ensuring global feature acquisition. Subsequently, to restore texture details blurred by the blind-spot structures, we introduce a texture generation diffusion structure during model training, achieving a balance between large-scale blind-spot characteristics and local rich texture details. Experimental results demonstrate that our approach outperforms other self-supervised denoising methods, even some methods leveraging unpaired images, without the need for parameters related to on-orbit satellite detectors.

Published
2024
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