Efficient Super Resolution for Large-Scale Images Using Attentional GAN

Pathak, Harsh Nilesh; Li, Xinxin; Minaee, Shervin; Cowan, Brooke

doi:10.1109/bigdata.2018.8622477

Cited by 23 publications

(18 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Figure 4 shows an example. Pathak et al (2018) outlined a working model optimized for improving Expedia Group lodging images. Previous work on image resolution focused on smaller images with resolutions under 500px, whereas travel pictures have more than 2000px and, thus, require different techniques.…”

Section: Deduplication Of Imagesmentioning

confidence: 99%

The Voice of Major E-Tourism Players: An Expedia Group Perspective

Krasnodebski¹

2021

Handbook of E-Tourism

View full text Add to dashboard Cite

show abstract

Section: Deduplication Of Imagesmentioning

confidence: 99%

The Voice of Major E-Tourism Players: An Expedia Group Perspective

Krasnodebski¹

2021

Handbook of E-Tourism

View full text Add to dashboard Cite

show abstract

“…In ref. [23], SRR was performed using attentional super-resolution GAN. The authors proposed a method of performing SRR using images of pixel size 2000×1340, which are almost four times larger than the existing images of pixel size, 500×500.…”

Section: Related Work a Super-resolution Reconstructionmentioning

confidence: 99%

“…With deeplearning method [17][18][19][20][21][22][23] The method extracts suitable features in various camera settings and environments Large-scale data collection, training, and processing require more time.…”

Section: Visible-light Imagementioning

confidence: 99%

Thermal Image Reconstruction Using Deep Learning

et al. 2020

View full text Add to dashboard Cite

A high-resolution thermal camera is very expensive and is thus difficult to be used. Furthermore, thermal images become blurred in various cases of object motion, camera shaking, and camera defocusing. To solve these problems, a previous super-resolution restoration (SRR) technique converting a thermal image acquired by a low-resolution camera into a highresolution one, and a thermal image deblurring method have been researched. However, existing studies were performed based on 1-channel (grayscale) images. In addition, a large-sized and whole image has been used in the existing thermal image deblurring methods, which causes lower deblurring performance. In this study, we propose novel SRR and deblurring methods. The proposed deblurring method is conducted based on small region images. The proposed methods are also conducted using 3-channel (color) thermal images and generative adversarial networks. In addition, the performances of this method are compared in various color spaces (RGB, Gray, HLS, HSV, Lab, Luv, XYZ, YCrCb), image sizes, and thermal databases. Through experiments using selfcollected databases and open databases, it was confirmed that the proposed methods show better performance than the state-ofthe-art methods. INDEX TERMS thermal image; super-resolution reconstruction; deep learning; generative adversarial network; image deblurring.

show abstract

“…Although these methods have achieved excellent results according to PSNR and SSIM, the visual perception quality is still poor. Several models follow SRGANs [19] to combine the GAN and perceptual loss [21] to obtain ISR, which can generate improved results in visual quality [22][23][24][25].…”

Section: Related Workmentioning

confidence: 99%

A multiresolution mixture generative adversarial network for video super-resolution

Tian

Wang

et al. 2020

PLoS ONE

View full text Add to dashboard Cite

Generative adversarial networks (GANs) have been used to obtain super-resolution (SR) videos that have improved visual perception quality and more coherent details. However, the latest methods perform poorly in areas with dense textures. To better recover the areas with dense textures in video frames and improve the visual perception quality and coherence in videos, this paper proposes a multiresolution mixture generative adversarial network for video super-resolution (MRMVSR). We propose a multiresolution mixture network (MRMNet) as the generative network that can simultaneously generate multiresolution feature maps. In MRMNet, the high-resolution (HR) feature maps can continuously extract information from low-resolution (LR) feature maps to supplement information. In addition, we propose a residual fluctuation loss function for video super-resolution. The residual fluctuation loss function is used to reduce the overall residual fluctuation on SR and HR video frames to avoid a scenario where local differences are too large. Experimental results on the public benchmark dataset show that our method outperforms the state-of-the-art methods for the majority of the test sets.

show abstract

Efficient Super Resolution for Large-Scale Images Using Attentional GAN

Cited by 23 publications

References 25 publications

The Voice of Major E-Tourism Players: An Expedia Group Perspective

The Voice of Major E-Tourism Players: An Expedia Group Perspective

Thermal Image Reconstruction Using Deep Learning

A multiresolution mixture generative adversarial network for video super-resolution

Contact Info

Product

Resources

About