Single ISAR image enhancement based on convolutional neural network

Mingrui Qi; Chen Chen; Qingwei Yang

doi:10.1117/12.2617683

15 February 2022 Single ISAR image enhancement based on convolutional neural network

Mingrui Qi, Chen Chen, Qingwei Yang

Proceedings Volume 12166, Seventh Asia Pacific Conference on Optics Manufacture and 2021 International Forum of Young Scientists on Advanced Optical Manufacturing (APCOM and YSAOM 2021); 121665R (2022) https://doi.org/10.1117/12.2617683
Event: Seventh Asia Pacific Conference on Optics Manufacture and 2021 International Forum of Young Scientists on Advanced Optical Manufacturing (APCOM and YSAOM 2021), 2021, Hong Kong, Hong Kong

Abstract

Compared with traditional sparsity-driven methods, inverse synthetic aperture radar (ISAR) image enhancement method based on convolutional neural networks (CNNs) have outstanding performance in recent research, which improved the resolution of reconstructed image significantly with higher imaging efficiency. However, recently developed ISAR image enhancement methods based on neural networks are only effective in the same scenarios where the training data was generated. Additionally, all these method adopted the mean-squared error as the loss function, causing the reconstructed ISAR image to lose high-frequency information and fail to capture appropriate details. To address these limitations, a single ISAR image enhancement framework based on a modified super-resolution convolutional neural network (SRCNN) is proposed in this paper. The ISAR image enhancement processing framework were improved to minimize the influence of the fixed imaging model. A combined loss function, composed of the structural similarity (SSIM) loss and the L1 loss functions, was adopted in the proposed framework to retain the high-frequency information and the luminance information of the ISAR image, while improving the resolution. Through quantitative analysis of experimental results by using different quality evaluation indicators, it demonstrated that compared with extant methods, the proposed framework provides reconstructed ISAR images with higher resolution and definition over a range of different scenarios.

Citation Download Citation

Mingrui Qi, Chen Chen, and Qingwei Yang "Single ISAR image enhancement based on convolutional neural network", Proc. SPIE 12166, Seventh Asia Pacific Conference on Optics Manufacture and 2021 International Forum of Young Scientists on Advanced Optical Manufacturing (APCOM and YSAOM 2021), 121665R (15 February 2022); https://doi.org/10.1117/12.2617683

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KEYWORDS

Image enhancement

Scattering

Image processing

Image quality

Image resolution

Convolutional neural networks

Data modeling

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