Remote Sensing Imagery Super Resolution Based on Adaptive Multi-Scale Feature Fusion Network

Wang, Xinying; Wu, Yao; Yang, Ming; Lv, Hui

doi:10.3390/s20041142

Cited by 29 publications

(14 citation statements)

References 29 publications

(32 reference statements)

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“…With the continuous development of visualization technology, many researchers are trying new approaches to ultrasound-guided pudendal nerve block. In clinical studies, the application of ultrasound in the treatment of ultrasoundguided pudendal nerve block has been consistently approved [13][14][15]. Ultrasound imaging is used to directly observe the movement process of the puncture needle, and it can visually show the position relationship between the needle entry route and the surrounding tissues, muscles, nerves, and blood vessels.…”

Section: Discussionmentioning

confidence: 99%

Ultrasound Image-Guided Pudendal Nerve Block on Analgesic Effect of Perineotomy under Optimized Fast Super Resolution Reconstructed Convolutional Neural Network Algorithm

Zhang

Xiao-ying

Zhao

et al. 2021

Scientific Programming

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This work was aimed to study the analgesic effect of pudendal nerve block on obstetrics and gynecology under the guidance of ultrasound image based on optimized fast super resolution reconstructed convolutional neural network (FSRCNN) algorithm. A total of 110 primiparas from hospital who gave birth through vagina were randomly rolled into experimental group (55 cases) and control group (55 cases). The optimized FSRCNN algorithm was constructed, compared with the FSRCNN algorithm and the Bicubic algorithm and applied to 110 cases of maternal patients undergoing perineotomy under ultrasound image-guided pudendal nerve block. Visual analogue scoring (VAS), incision suture pain VAS score, occurrence of complications, puerpera labor time, and newborn weight were recorded and compared, so did Apgar score of newborns, numbness of maternal thigh, recovery of puncture site, and satisfaction of maternal analgesia. The results showed that the peak signal-to-noise ratio (PSNR) of the high-resolution image reconstructed by the FSRCNN algorithm was 32.68 dB and that reconstructed by the optimized FSRCNN algorithm was 32.19 dB. The PSNR of the Bicubic algorithm processed image was 28.51 dB. In the lateral resection of episiotomy in the second stage of labor, the visual analog score (2.3 ± 1.5 points) of the experimental group was inferior to that of the control group (7.1 ± 2.6 points) ( P < 0.05 ). The visual analogue score of stitch pain (1.3 ± 0.8 points) was also inferior to that of the control group (5.2 ± 1.9 points) ( P < 0.05 ). Moreover, the satisfaction of the parturients in the experimental group (9.86 ± 0.41 points) was considerably superior to that of the control group (7.36 ± 1.25 points) ( P < 0.05 ). In short, the optimized FSRCNN algorithm had a short training time and good reconstruction effect. Ultrasound-guided pudendal nerve block had a substantial analgesic effect on the second stage of labor and improved parturients’ satisfaction.

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Section: Discussionmentioning

confidence: 99%

Ultrasound Image-Guided Pudendal Nerve Block on Analgesic Effect of Perineotomy under Optimized Fast Super Resolution Reconstructed Convolutional Neural Network Algorithm

Zhang

Xiao-ying

Zhao

et al. 2021

Scientific Programming

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“…Spatial SR research utilizes either frequency-domain-based, interpolationbased, regularization-based signal processing techniques, or machine-learning methods (for reviews, see [30][31][32]). Recently, with the development of deep learning techniques, convolutional neural networks (CNN) have widely been utilized in spatial SR research (see, for example, [33][34][35][36][37][38]; for review, see [28]). In line with spatial, spectral SR research, modeling the mapping from coarser scale spectrum (usually, RGB) to finer one (usually, hyperspectral images of over 30 spectral channels) [39], is also nowadays quickly developing.…”

Section: Introductionmentioning

confidence: 99%

CNN-Based Spectral Super-Resolution of Panchromatic Night-Time Light Imagery: City-Size-Associated Neighborhood Effects

Rybnikova

Mirkes

Gorban

2021

Sensors

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Data on artificial night-time light (NTL), emitted from the areas, and captured by satellites, are available at a global scale in panchromatic format. In the meantime, data on spectral properties of NTL give more information for further analysis. Such data, however, are available locally or on a commercial basis only. In our recent work, we examined several machine learning techniques, such as linear regression, kernel regression, random forest, and elastic map models, to convert the panchromatic NTL images into colored ones. We compared red, green, and blue light levels for eight geographical areas all over the world with panchromatic light intensities and characteristics of built-up extent from spatially corresponding pixels and their nearest neighbors. In the meantime, information from more distant neighboring pixels might improve the predictive power of models. In the present study, we explore this neighborhood effect using convolutional neural networks (CNN). The main outcome of our analysis is that the neighborhood effect goes in line with the geographical extent of metropolitan areas under analysis: For smaller areas, optimal input image size is smaller than for bigger ones. At that, for relatively large cities, the optimal input image size tends to differ for different colors, being on average higher for red and lower for blue lights. Compared to other machine learning techniques, CNN models emerged comparable in terms of Pearson’s correlation but showed performed better in terms of WMSE, especially for testing datasets.

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“…Table 2 shows the quantitative results of scale factors ×4. Among them, Bicubic, FSRCNN [42], SRResnet [13], RCAN [46], and SRGAN [13] are the advanced SR methods, and LGCNet [78], DMCN [1], DRSEN [79], DCM [84], and AMFFN [85] are remote sensing SR methods. The results of the advanced SR methods are tested with the pre-trained model of the DIV2K [86] dataset.…”

Section: Application Of Remote Sensing Imagementioning

confidence: 99%

Saliency-Guided Remote Sensing Image Super-Resolution

Liu

Zhao

et al. 2021

Remote Sensing

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Deep learning has recently attracted extensive attention and developed significantly in remote sensing image super-resolution. Although remote sensing images are composed of various scenes, most existing methods consider each part equally. These methods ignore the salient objects (e.g., buildings, airplanes, and vehicles) that have more complex structures and require more attention in recovery processing. This paper proposes a saliency-guided remote sensing image super-resolution (SG-GAN) method to alleviate the above issue while maintaining the merits of GAN-based methods for the generation of perceptual-pleasant details. More specifically, we exploit the salient maps of images to guide the recovery in two aspects: On the one hand, the saliency detection network in SG-GAN learns more high-resolution saliency maps to provide additional structure priors. On the other hand, the well-designed saliency loss imposes a second-order restriction on the super-resolution process, which helps SG-GAN concentrate more on the salient objects of remote sensing images. Experimental results show that SG-GAN achieves competitive PSNR and SSIM compared with the advanced super-resolution methods. Visual results demonstrate our superiority in restoring structures while generating remote sensing super-resolution images.

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Remote Sensing Imagery Super Resolution Based on Adaptive Multi-Scale Feature Fusion Network

Cited by 29 publications

References 29 publications

Ultrasound Image-Guided Pudendal Nerve Block on Analgesic Effect of Perineotomy under Optimized Fast Super Resolution Reconstructed Convolutional Neural Network Algorithm

Ultrasound Image-Guided Pudendal Nerve Block on Analgesic Effect of Perineotomy under Optimized Fast Super Resolution Reconstructed Convolutional Neural Network Algorithm

CNN-Based Spectral Super-Resolution of Panchromatic Night-Time Light Imagery: City-Size-Associated Neighborhood Effects

Saliency-Guided Remote Sensing Image Super-Resolution

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