Physics-Guided Optical Simulation and PSF Analysis for Remote Sensing Images Deblurring

• Published in: IEEE transactions on geoscience and remote sensing Vol. 62; pp. 1 - 15
• Main Authors: Ji, Fan, Wang, Jing, Cui, Shuang, Li, Jiangmeng, Tang, Xiongxin, Xu, Fanjiang
• Format: Journal Article
• Language: English
• Published: New York IEEE 2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Blurring; Datasets; Deconvolution; Deep learning; Deep Wiener deconvolution; Effectiveness; Image processing; Image restoration; Impact analysis; Kernel; Noise; Objective function; Optical imaging; optical simulation; Physics; point spread function (PSF); Qualitative analysis; Remote sensing; remote sensing image (RSI) deblurring; Satellite imagery; Satellites; Spatial discrimination learning; Synthetic data; Training; Zernike polynomials
• ISSN: 0196-2892; 1558-0644
• DOI: 10.1109/TGRS.2024.3426094

The presence of blur is prevalent in satellite remote sensing images (RSIs), and its detrimental impact on downstream applications cannot be overlooked. Current deep learning approaches for image deblurring have gained substantial attention due to their effectiveness and fast inference speed. However, these methods often heavily rely on extensive paired training datasets and lack interpretability. Existing deblurring datasets primarily include regular scenes while remote sensing images exhibit distinct blurring mechanisms. Consequently, deep learning methods lacking prior physical knowledge can only tackle the image deblurring problem in specific scenarios, but hard to achieve satisfactory results on remote sensing images. To address these problems, it is essential to construct a remote sensing image dataset that incorporates the realistic causes of blurriness and integrate prior knowledge into the methods. In this work, we first analyze the satellite imaging system and use Zernike polynomials to approximate the optical aberrations to simulate the RSI blurring process which ensures the proposed dataset adhering solid physical principles. Moreover, we propose a novel physics-guided RSI deblurring (PGRSID) network that integrates an explicit Wiener deconvolution process in both spatial and deep feature space. This integration better leverages the physical interpretation to facilitate effective learning for the RSI deblurring network. We further incorporate denoise loss and cycle consistency loss in the objective function to facilitate the model's learning process for RSI deblurring. Extensive experiments are conducted on both our synthetic dataset and real GF-1A/PMS data. Qualitative and quantitative experiment results highlight the effectiveness and superiority of our physics-guided deblurring network for satellite RSI.