Satellite Imagery Super-Resolution Using Squeeze-and-Excitation-Based GAN

Moustafa, Marwa; Sayed, Sayed A.

doi:10.1007/s42405-021-00396-6

Cited by 18 publications

(9 citation statements)

References 41 publications

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“…Dharejo et al used Wavelet Transform (WT) characteristics into a transferred GAN to eliminate artifacts to improve quality of predicted remote sensing images [83]. Moustafa et al embedded squeeze-and-excitation blocks and residual blocks into a generator to obtain more highfrequency details [84]. Besides, Wasserstein distance is used to enhance the stability of training a remote sensing superresolution model [84].…”

Section: Popular Gans For Image Applicationsmentioning

confidence: 99%

“…Moustafa et al embedded squeeze-and-excitation blocks and residual blocks into a generator to obtain more highfrequency details [84]. Besides, Wasserstein distance is used to enhance the stability of training a remote sensing superresolution model [84]. To address pseudo-textures problem, a saliency analysis is fused with a GAN to obtain a salient map that can be used to distinguish difference between a discriminator and a generator [85].…”

Section: Popular Gans For Image Applicationsmentioning

confidence: 99%

“…Cheng 2) Supervised GANs based prior knowledge for image super-resolution: It is known that combination of discriminative method and optimization can make a tradeoff between efficiency and performance [102]. Guan CGAN CGAN with local saliency maps for retinal image super-resolution PGGAN [77] GAN Progressive growing GAN for image super-resolution ESRGAN [78] SRGAN [93] SRGAN with Residual-in-Residual Dense Block (RRDB) and relativistic discriminator for image super-resolution MPDGAN [79] GAN GAN with multi-discriminators for image super-resolution ESRGAN+ [80] ESRGAN ESRGAN with Residual-in-Residual Dense Residual Block (RRDRB) for image super-resolution SPGAN [81] GAN GAN with identity-based discriminator for face image super-resolution MLGE [86] LAPGAN LAPGAN with edge information for face image super-resolution SD-GAN [85] GAN GAN for remote sensing image super-resolution PathSRGAN [91] SRGAN SRGAN with RRDB for cytopathology image super-resolution Enlighten-GAN [82] GAN GAN with enlighten block for remote sensing image super-resolution TWIST-GAN [83] GAN GAN with wavelet transform (WT) for remote sensing image super-resolution SCSE-GAN [84] GAN GAN with SCSE block for image super-resolution MFAGAN [92] GAN GAN with multi-scale feature aggregation net for image super-resolution TGAN [87] GAN GAN with visual tracking and attention networks for image super-resolution DGAN [88] GAN GAN with disentangled representation learning and anisotropic BRDF reconstruction for image super-resolution DMGAN [89] GAN GAN with two same generators for image super-resolution G-GANISR [90] GAN GAN with gradual learning for image super-resolution SRGAN [93] GAN GAN with deep ResNet for image super-resolution RaGAN [94] GAN GAN with relativistic discriminator for image super-resolution LE-GAN [95] GAN GAN with a latent encoder for realistic hyperspectral image super-resolution NCSR [96] GAN GAN with a noise conditional layer for image super-resolution Beby-GAN [97] GAN GAN with a region-aware adversarial learning strategy for image super-resolution MA-GAN [98] GAN GAN with pyramidal convolution for image super-resolution CMRI-CGAN …”

Section: Popular Gans For Image Applicationsmentioning

confidence: 99%

See 2 more Smart Citations

Generative Adversarial Networks for Image Super-Resolution: A Survey

Tian¹,

Zhang²,

Lin³

et al. 2022

Preprint

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Single image super-resolution (SISR) has played an important role in the field of image processing. Recent generative adversarial networks (GANs) can achieve excellent results on low-resolution images with small samples. However, there are little literatures summarizing different GANs in SISR. In this paper, we conduct a comparative study of GANs from different perspectives. We first take a look at developments of GANs. Second, we present popular architectures for GANs in big and small samples for image applications. Then, we analyze motivations, implementations and differences of GANs based optimization methods and discriminative learning for image super-resolution in terms of supervised, semi-supervised and unsupervised manners. Next, we compare performance of these popular GANs on public datasets via quantitative and qualitative analysis in SISR. Finally, we highlight challenges of GANs and potential research points for SISR.

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Section: Popular Gans For Image Applicationsmentioning

confidence: 99%

Section: Popular Gans For Image Applicationsmentioning

confidence: 99%

Section: Popular Gans For Image Applicationsmentioning

confidence: 99%

See 1 more Smart Citation

Generative Adversarial Networks for Image Super-Resolution: A Survey

Tian¹,

Zhang²,

Lin³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Because the GAN consists of the generator and discriminator, the training of these two models provides realistic image outputs. In [46], the authors achieved the best measure performance using GAN in superresolution GAN (SRGAN) and image restoration problem by recovering information in detail [47]. SRGAN can be used in different fields, such as medical images.…”

Section: Denoising Images Based On Ganmentioning

confidence: 99%

Resolving Interferometric SAR Speckle Problem Using Adaptive GAN

2022

IJIES

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The interference of reflected waves from multiple elementary scatterers produces speckle, which appears as a granular noise in synthetic aperture radar (SAR) images. These speckles in SAR images cause difficulty in image interpretation, which reduces the effectiveness of image segmentation and classification. In this paper, we propose an effective solution using generative adversarial networks (GAN) to decrease speckle noise while preserving texture features.The convolutional block attention module (CBAM) boosts the essential features in upsampled data and supports the creation of a final denoised real InSAR data.The proposed deep-learning method proved that CBAM enhanced the peak signal-to-noise ratio using the GAN. Furthermore, CBAM improved structural similarity (SSIM) to 99 and achieved the minimum mean squared error. The despeckle performance was enhanced using GAN-ResUNet in which the SSIM equals 0.999. The denoising performance proved that the use of GAN with CBAM as the generator inspired by ResUNeT achieved the best performance compared to other experiments results.

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“…Deep learning uses multilayer neural networks in many applications [8,9] such as: object detection [10], image classification, image denoising [11], medical image segmentation [12], image super-resolution [13][14][15], and depth prediction in stereo and monocular images [16]. Recently, several researches have investigated deep learning algorithms to improve building footprint extraction [17][18][19] either using CNN or a fully convolutional neural network.…”

Section: Introductionmentioning

confidence: 99%

Building Footprint Extraction in Dense Area from LiDAR Data using Mask R-CNN

Mohamed¹,

Mahmoud²,

Moustafa³

et al. 2022

IJACSA

Self Cite

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Building footprint extraction is an essential process for various geospatial applications. The city management is entrusted with eliminating slums, which are increasing in rural areas. Compared with more traditional methods, several recent research investigations have revealed that creating footprints in dense areas is challenging and has a limited supply. Deep learning algorithms provide a significant improvement in the accuracy of the automated building footprint extraction using remote sensing data. The mask R-CNN object detection framework used to effectively extract building in dense areas sometimes fails to provide an adequate building boundary result due to urban edge intersections and unstructured buildings. Thus, we introduced a modified workflow to train ensemble of the mask R-CNN using two backbones ResNet (34, 101). Furthermore, the results were stacked to fine-grain the structure of building boundaries. The proposed workflow includes data preprocessing and deep learning, for instance, segmentation was introduced and applied to a light detecting and ranging (LiDAR) point cloud in a dense rural area. The outperformance of the proposed method produced better-regularized polygons that obtained results with an overall accuracy of 94.63%.

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Satellite Imagery Super-Resolution Using Squeeze-and-Excitation-Based GAN

Cited by 18 publications

References 41 publications

Generative Adversarial Networks for Image Super-Resolution: A Survey

Generative Adversarial Networks for Image Super-Resolution: A Survey

Resolving Interferometric SAR Speckle Problem Using Adaptive GAN

Building Footprint Extraction in Dense Area from LiDAR Data using Mask R-CNN

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