Research on Image Super Resolution Reconstruction Model Based on Deep Learning

Zeng, Yuhao; Peng, Sijie; Ruan, Shengyi

doi:10.1109/aeeca49918.2020.9213560

Cited by 5 publications

(1 citation statement)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Especially, when distinguishing materials of low atomic numbers, the materials tend to disappear in the image because spatial resolution is lacking. Super-resolution [3] can provide a solution to this problem and is also beneficial to the later use and processing of multi-energy CT images.…”

Section: Introductionmentioning

confidence: 99%

UNet-ESPC-Cascaded Super-Resolution Reconstruction in Spectral CT

Wang

et al. 2020

2020 15th IEEE International Conference on Signal Processing (ICSP)

View full text Add to dashboard Cite

Spectral CT based on photon counting detectors is a promising imaging modality since it provides the possibility of both obtaining CT images from multi-energy bins with a single Xray exposure and allowing low-dose imaging. However, the image quality such as spatial resolution reconstructed from multiple energy bins is degraded because of the use of narrow energy bins in spectral CT. We propose to use deep learning methods for super-resolution reconstruction of spectral CT images. To this end, we introduce an UNet-ESPC-cascaded model and perform a patch-based training to obtain the optimal parameters of the model. Experimental results on physical phantom datasets demonstrated that our deep learning based reconstruction method can reduce the F form error between the reconstructed superresolution CT image and the ground truth, by 11.6% and 5.66% with respect to respectively bilinear-interpolation-based reconstruction and iterative back projection methods. Our method achieves best results with a patch size of 20 and a stride of 15.

show abstract

Section: Introductionmentioning

confidence: 99%

UNet-ESPC-Cascaded Super-Resolution Reconstruction in Spectral CT

Wang

et al. 2020

2020 15th IEEE International Conference on Signal Processing (ICSP)

View full text Add to dashboard Cite

show abstract

Enhancing Upscaled Image Resolution Using Hybrid Generative Adversarial Network-Enabled Frameworks

Geetha,

Jebamalar,

Shiney

et al. 2024

IEEE Access

View full text Add to dashboard Cite

Upscaling images typically depends on facial features, such as facial geometry details or references, to rebuild reasonable details. The low-quality input images cannot offer accurate geometric details, and the high-quality details are obscure, limiting performance in practical conditions. This work addresses the problem using an enhanced version of the generative adversarial network (GAN) model that uses rich and varied facial features incorporated into the pretrained StyleGAN2 for face restoration. The generated facial features are integrated into the facial restoration process using spatial feature transform layers to achieve facial details and color quality to improve reliability. In particular, the image background is upsampled using a modified enhanced super-resolution GAN trained in parallel to remove noise while recreating a high-resolution image from low-quality input. The upscale image resolution GAN is used to enlarge the image for a clear view without sacrificing the original image quality. Finally, by combining the upscaled background and restored faces, the hybrid GAN-enabled framework can obtain high-resolution upscaled images. An experimental comparison has been conducted using the Flickr-Faces-HQ Dataset collected from Kaggle. The findings indicate that the suggested framework outperforms existing methods in terms of reconstruction, adversarial, and facial component loss metrics as well as similarity indexes.

show abstract

Group Feature Information Distillation Network For Single Image Super-resolution

Chen

2021

2021 7th International Conference on Computer and Communications (ICCC)

View full text Add to dashboard Cite

Research on Image Super Resolution Reconstruction Model Based on Deep Learning

Cited by 5 publications

References 3 publications

UNet-ESPC-Cascaded Super-Resolution Reconstruction in Spectral CT

UNet-ESPC-Cascaded Super-Resolution Reconstruction in Spectral CT

Enhancing Upscaled Image Resolution Using Hybrid Generative Adversarial Network-Enabled Frameworks

Group Feature Information Distillation Network For Single Image Super-resolution

Contact Info

Product

Resources

About