Least Squares Relativistic Generative Adversarial Network for Perceptual Super-Resolution Imaging

Zhang, Sanyou; Cheng, Deqiang; Jiang, Daihong

doi:10.1109/access.2020.3030044

Cited by 7 publications

(4 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A CGAN has a simple and straightforward architecture, yet can produce images similar to real ones. Compared to other GAN architectures that may produce better quality images, such as the least-squares generative adversarial network (LSGAN) [52] and information maximizing GAN (InfoGAN) [53], these architectures have large computational budgets and generating images is time-consuming, whereas CGANs are simpler and do not require long computation times. They can synthesize good-quality images from the original dataset.…”

Section: Discussionmentioning

confidence: 99%

COVID-CGAN: Efficient Deep Learning Approach for COVID-19 Detection Based on CXR Images Using Conditional GANs

et al. 2021

View full text Add to dashboard Cite

COVID-19, a novel coronavirus infectious disease, has spread around the world, resulting in a large number of deaths. Due to a lack of physicians, emergency facilities, and equipment, medical systems have been unable to treat all patients in many countries. Deep learning is a promising approach for providing solutions to COVID-19 based on patients’ medical images. As COVID-19 is a new disease, its related dataset is still being collected and published. Small COVID-19 datasets may not be sufficient to build powerful deep learning detection models. Such models are often over-fitted, and their prediction results cannot be generalized. To fill this gap, we propose a deep learning approach for accurately detecting COVID-19 cases based on chest X-ray (CXR) images. For the proposed approach, named COVID-CGAN, we first generated a larger dataset using generative adversarial networks (GANs). Specifically, a customized conditional GAN (CGAN) was designed to generate the target COVID-19 CXR images. The expanded dataset, which contains 84.8% generated images and 15.2% original images, was then used for training five deep detection models: InceptionResNetV2, Xception, SqueezeNet, VGG16, and AlexNet. The results show that the use of the synthetic CXR images, which were generated by the customized CGAN, helped all deep learning models to achieve high detection accuracies. In particular, the highest accuracy was achieved by the InceptionResNetV2 model, which was 99.72% accurate with only ten epochs. All five models achieved kappa coefficients between 0.81 and 1, which is interpreted as an almost perfect agreement between the actual labels and the detected labels. Furthermore, the experiment showed that some models were faster yet smaller compared to the others but could still achieve high accuracy. For instance, SqueezeNet, which is a small network, required only three minutes and achieved comparable accuracy to larger networks such as InceptionResNetV2, which needed about 143 min. Our proposed approach can be applied to other fields with scarce datasets.

show abstract

Section: Discussionmentioning

confidence: 99%

COVID-CGAN: Efficient Deep Learning Approach for COVID-19 Detection Based on CXR Images Using Conditional GANs

et al. 2021

View full text Add to dashboard Cite

show abstract

“…The training process of standard GAN (StdGAN) is unstable because the architecture of the discriminative network is nontransformed and saturated [46,52]. RaGAN is an extension of StdGAN where the discriminative network takes a relativistic and non-saturating form [52][53][54]. Therefore, rather than measuring the probability that the input land cover map M is realistic and generated in StdGAN, RaGAN predicts whether a land cover map Mr from the training dataset is more realistic than a fake one, Mf (Mf=G(I)).…”

Section: Discriminative Network Dsgsmentioning

confidence: 99%

Spectral–Spatial Generative Adversarial Network for Super-Resolution Land Cover Mapping With Multispectral Remotely Sensed Imagery

Shang

Jiang

Ling

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

View full text Add to dashboard Cite

Super-resolution mapping (SRM) can effectively predict the spatial distribution of land cover classes within mixed pixels at a higher spatial resolution than the original remotely sensed imagery. The uncertainty of land cover fraction errors within mixed pixels is one of the most important factors affecting SRM accuracy. Studies have shown that SRM methods using deep learning techniques have significantly improved land cover mapping accuracy but have not coped well with spectral-spatial errors. This study proposes an end-to-end SRM model using a spectral-spatial generative adversarial network (SGS) with the direct input of multispectral remotely sensed imagery, which deals with spectral-spatial error. The proposed SGS comprises three parts: (1) Cube-based convolution for spectral unmixing is adopted to generate land cover fraction images. (2) A residual-inresidual dense block fully and jointly considers spectral and spatial information and reduces spectral errors. (3) A relativistic average GAN is designed as a backbone to further improve super-resolution performance and reduce spectral-spatial errors. SGS was tested in one synthetic and two realistic experiments with multi-/hyper-spectral remotely sensed imagery as the input, comparing the results with those of hard classification and several classic SRM methods. The results showed that SGS performed well at reducing land cover fraction errors, reconstructing spatial details, removing unpleasant and unrealistic land cover artifacts, and eliminating false recognition.

show abstract

“…? Also, in recent years, GAN based architecture: Relativistic GAN , 16 ESRGAN ; 9 and channel attention-based architecture: RGAN 17 provide promising results for the problem of SISR in terms of image quality.…”

Section: Related Workmentioning

confidence: 99%

Computational efficient deep learning-based super resolution approach

Artusi

2023

Real-Time Processing of Image, Depth and Video Information 2023

View full text Add to dashboard Cite

Deep learning-based Single Image Super-Resolution (SISR) has recently provided great performances when compared with state-of-the-art approaches. However, these performances are usually at the cost of high computational complexity and memory management, even at the inference stage. In this paper, we aim to reduce the structural complexity of a state-of-the-art Deep Neural Networks (DNN)1 approach to propose a cost-effective solution to the problem of SISR. We have investigated how the different components of the model (baseline) may affect the overall complexity while minimizing the negative effect on its quality performances. This has provided a solution, which yields quality performances comparable to the baseline model, while approximately reducing more than one order of magnitude the number of parameters, the spatial complexity (GPU memory) up to 1/6 and inference time by 1/2.

show abstract

Least Squares Relativistic Generative Adversarial Network for Perceptual Super-Resolution Imaging

Cited by 7 publications

References 39 publications

COVID-CGAN: Efficient Deep Learning Approach for COVID-19 Detection Based on CXR Images Using Conditional GANs

COVID-CGAN: Efficient Deep Learning Approach for COVID-19 Detection Based on CXR Images Using Conditional GANs

Spectral–Spatial Generative Adversarial Network for Super-Resolution Land Cover Mapping With Multispectral Remotely Sensed Imagery

Computational efficient deep learning-based super resolution approach

Contact Info

Product

Resources

About