Polarimetric SAR Image Super-Resolution VIA Deep Convolutional Neural Network

Lin, Liupeng; Li, Jie; Yuan, Qiangqiang; Shen, Huanfeng

doi:10.1109/igarss.2019.8898160

Cited by 11 publications

(5 citation statements)

References 8 publications

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“…Such an approach could be very promising once the method is extended to off-grid scenarios and with un-paired data training. When available also polarimetric information can support the development of super-resolution methods, as in [19], where the authors propose a full-polarimetric SAR image super-resolution reconstruction method combining a CNN and residual compensation. The advantages of the deep CNN for nonlinear model fitting are exploited in order to perform super-resolution reconstruction on low-resolution fullpolarimetric SAR images, and then a residual compensation is applied to the network reconstruction results.…”

Section: Introductionmentioning

confidence: 99%

Super-Resolution of Synthetic Aperture Radar Complex Data by Deep-Learning

et al. 2023

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One of the greatest limitations of Synthetic Aperture Radar imagery is the capability to obtain an arbitrarily high spatial resolution. Indeed, despite optical sensors, this capability is not just limited by the sensor technology. Instead, improving the SAR spatial resolution requires large transmitted bandwidth and relatively long synthetic apertures that for regulatory and practical reasons are impossible to be met. This issue gets particularly relevant when dealing with Stripmap mode acquisitions and with relatively low carrier frequency sensors (where relatively large bandwidth signals are more difficult to be transmitted). To overcome this limitation, in this paper a deep learning based framework is proposed to enhance the spatial resolution of low-resolution SAR images while retaining the complex image accuracy. Results on simulated and real SAR data demonstrate the effectiveness of the proposed framework.

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Section: Introductionmentioning

confidence: 99%

Super-Resolution of Synthetic Aperture Radar Complex Data by Deep-Learning

et al. 2023

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“…The potential to obtain high-definition images through image super-resolution reconstruction technology has attracted the attention of many researchers ( Lin et al., 2019 ; Yang et al., 2020 ; Dreier et al., 2021 ; Wei et al., 2022 ), but no reconstruction or application of laser confocal images has been reported in the literature. GAN and residual networks (ResNet) are two representative deep-learning networks.…”

Section: Introductionmentioning

confidence: 99%

“…The potential to obtain high-definition images through image super-resolution reconstruction technology has attracted the attention of many researchers (Lin et al, 2019;Yang et al, 2020;Dreier et al, 2021;Wei et al, 2022) SRGAN has the same overall structure as GAN. It is composed of a generative network and a discriminant network, and the perceived loss is used as the loss function (Ledig et al, 2017;Li and Zhang, 2022;Singla et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Super-resolution reconstruction, recognition, and evaluation of laser confocal images of hyperaccumulator Solanum nigrum endocytosis vesicles based on deep learning: Comparative study of SRGAN and SRResNet

Liu

et al. 2023

Front. Plant Sci.

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It is difficult for laser scanning confocal microscopy to obtain high- or ultra-high-resolution laser confocal images directly, which affects the deep mining and use of the embedded information in laser confocal images and forms a technical bottleneck in the in-depth exploration of the microscopic physiological and biochemical processes of plants. The super-resolution reconstruction model (SRGAN), which is based on a generative adversarial network and super-resolution reconstruction model (SRResNet), which is based on a residual network, was used to obtain single and secondary super-resolution reconstruction images of laser confocal images of the root cells of the hyperaccumulator Solanum nigrum. Using the peak signal-to-noise ratio (PSNR), structural similarity (SSIM) and mean opinion score (MOS), the models were evaluated by the image effects after reconstruction and were applied to the recognition of endocytic vesicles in Solanum nigrum root cells. The results showed that the single reconstruction and the secondary reconstruction of SRGAN and SRResNet improved the resolution of laser confocal images. PSNR, SSIM, and MOS were clearly improved, with a maximum PSNR of 47.690. The maximum increment of PSNR and SSIM of the secondary reconstruction images reached 21.7% and 2.8%, respectively, and the objective evaluation of the image quality was good. However, overall MOS was less than that of the single reconstruction, the perceptual quality was weakened, and the time cost was more than 130 times greater. The reconstruction effect of SRResNet was better than that of SRGAN. When SRGAN and SRResNet were used for the recognition of endocytic vesicles in Solanum nigrum root cells, the clarity of the reconstructed images was obviously improved, the boundary of the endocytic vesicles was clearer, and the number of identified endocytic vesicles increased from 6 to 9 and 10, respectively, and the mean fluorescence intensity was enhanced by 14.4% and 7.8%, respectively. Relevant research and achievements are of great significance for promoting the application of deep learning methods and image super-resolution reconstruction technology in laser confocal image studies.

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“…In [4] a new approach that combines the advantages of multipleimage fusion with learning the low-to-high resolution mapping using deep networks, is defined. In [5], authors proposes a full-polarimetric SAR image SR reconstruction method based upon deep convolutional neural network for nonlinear model fitting and, then, apply residual compensation to network reconstruction results using low-resolution image information. So far, DL has been successfully applied to single image SR, which aims at reconstructing a high-resolution (HR) image from its low-resolution (LR) counterpart.…”

Section: Introductionmentioning

confidence: 99%

Super-Resolution of Synthetic Aperture Radar Complex Data by Deep-Learning

Addabbo

Bernardi

Biondi

et al. 2022

2022 IEEE 9th International Workshop on Metrology for AeroSpace (MetroAeroSpace)

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One of the greatest limitations of Synthetic Aperture Radar imagery is the capability to obtain an arbitrarily high spatial resolution. Indeed, despite optical sensors, this capability is not just limited by the sensor technology. Instead, improving the SAR spatial resolution requires large transmitted bandwidth and relatively long synthetic apertures that for regulatory and practical reasons are impossible to be met. This issue gets particularly relevant when dealing with Stripmap mode acquisitions and with relatively low carrier frequency sensors (where relatively large bandwidth signals are more difficult to be transmitted). To overcome this limitation, in this paper a deep learning based framework is proposed to enhance the SAR image spatial resolution while retaining the complex image accuracy. Results on simuated and real SAR data demonstrate the effectiveness of the proposed framework.

show abstract

Polarimetric SAR Image Super-Resolution VIA Deep Convolutional Neural Network

Cited by 11 publications

References 8 publications

Super-Resolution of Synthetic Aperture Radar Complex Data by Deep-Learning

Super-Resolution of Synthetic Aperture Radar Complex Data by Deep-Learning

Super-resolution reconstruction, recognition, and evaluation of laser confocal images of hyperaccumulator Solanum nigrum endocytosis vesicles based on deep learning: Comparative study of SRGAN and SRResNet

Super-Resolution of Synthetic Aperture Radar Complex Data by Deep-Learning

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