Multi-scale residual denoising GAN model for producing super-resolution CTA images

Li, Pengcheng; Li, Zhangyu; Pang, Xiongwen; Wang, Hui; Lin, Weiwei; Wu, Wentai

doi:10.1007/s12652-021-03009-y

Cited by 10 publications

(7 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Section: Popular Gans For Image Applicationsmentioning

confidence: 99%

“…In terms of complex damaged image restoration, GANs are good choices [117]. For instance, Li et al used a multiscale residual block and an attention mechanism in a GAN to remove noise and restore detailed information in CTA image super-resolution [117]. Nneji et al improved a VGG19 to fine-tune two sub-networks with a wavelet technique to simultaneously address COVID-19 image denoising and superresolution problems [118].…”

Section: Popular Gans For Image Applicationsmentioning

confidence: 99%

“…Motivated by that, GANs in semi-supervised ways are used for image super-resolution [121]. For instance, by maintaining the cycle-consistency of Wasserstein distance, a mapping from noisy low-resolution GAN GAN with pre-trained models for image super-resolution I-SRGAN [105] GAN GAN with infrared prior knowledge for image super-resolution on infrared image RankSRGAN [106] SRGAN SRGAN with ranker for image super-resolution GMGAN [107] GAN GAN with a novel quality loss for image super-resolution FSLSR [108] GAN GAN with fourier space losses for image super-resolution I-WAGAN [109] GAN GAN with improved wasserstein gradient penalty and perceptual loss for image super-resolution CESR-GAN [110] GAN GAN with a feature-based measurement loss function for image super-resolution RTSRGAN [112] SRGAN SRGAN for real time image super-resolution MSSRGAN [113] ESRGAN ESRGAN with denoising module for image super-resolution RSISRGAN [114] GAN GAN for image super-resolution on RSI JPLSRGAN [115] GAN GAN for license plate recognition and image super-resolution SRR-GAN [116] GAN GAN for image super-resolution on text images MRD-GAN [117] GAN GAN with attention mechanism for image super-resolution and denoising MESRGAN+ [118] ESRGAN ESRGAN with siamese network for image super-resolution and denoising. RealESRGAN [56] ESRGAN ESRGAN with pure synthetic data for blind image super-resolution SNPE-SRGAN [119] SRGAN SRGAN with SPNE for image super-resolution SOUP-GAN [120] GAN GAN with 3D MRI for image super-resolution images to high-resolution images was built [121].…”

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

View full text Add to dashboard Cite

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.

show abstract

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

“…These methods are ineffective for recreating high-quality Xray images with larger dimensions than ground-truth images and require high-quality edge detail reconstruction [9]. Dean Huntrin et al (2014) proposed a method for example-based medical image super-resolution and denoising that requires several time-consuming image preprocessing steps.…”

Section: Introductionmentioning

confidence: 99%

“…These methods are ineffective for recreating high‐quality X‐ray images with larger dimensions than ground‐truth images and require high‐quality edge detail reconstruction [9]. Dean Huntrin et al.…”

Section: Introductionmentioning

confidence: 99%

X‐Ray medical image super‐resolution via self‐organization neural networks and geometric directional gradient

Ahmadian

Reza-Alikhani

2022

IET Image Processing

View full text Add to dashboard Cite

Nowadays, due to the rapid development of the Internet and communication networks, remote disease diagnosis by physicians utilizing medical images such as X-rays have increased. However, the transmitted images lose their true quality and resolutions due to several factors, including noise and image compression techniques used to conserve memory space. This paper presents a novel clinical X-ray image enhancement method based on self-organizing neural networks, the ability of first-order derivative operators to detect edges, and improved classification methods by enhancing image features and extending the nearest neighbor algorithms selection of the best similarity feature vector among all sample feature vectors. The proposed method employs first-order image derivation operators to extract edge details and features from images. The feature vectors were preprocessed and segmented into various classes using self-organizing neural networks. The best matching and most similar vectors among the feature vectors are retrieved via the nearest neighbor algorithm. In contrast to conventional and contemporary image super-resolution techniques, the proposed method does not rely on the cost function. It is independent of error backpropagation methods because the competitive nature of the neural network is used to update the weight vectors rather than the gradient descent method. In addition, the performance and accuracy of the proposed image super-resolution method were improved by increasing the number of feature components in the feature vectors through the use of the two-dimensional image gradient. Furthermore, numerous image quality measurement criteria, such as structural similarity index (SSIM) and peak signalto-noise ratio (PSNR), were used to compare the reconstructed images of the proposed algorithm with those of other image enhancement algorithms. The results show that the proposed image enhancement algorithms' high-resolution and high-quality reconstructed image significantly outperformed many other medical image enhancement algorithms in reconstructing the edges and boundaries of objects with a smooth slope, as deemed by expert personnel and compared with image quality measurement criteria.

show abstract