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ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
Xintao Wang,Ke Yu,Shixiang Wu,Jinjin Gu,Yihao Liu,Chao Dong,Chen Change Loy,Yu Qiao,Xiaoou Tang +8 more
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This work thoroughly study three key components of SRGAN – network architecture, adversarial loss and perceptual loss, and improves each of them to derive an Enhanced SRGAN (ESRGAN), which achieves consistently better visual quality with more realistic and natural textures than SRGAN.Abstract:
The Super-Resolution Generative Adversarial Network (SRGAN) is a seminal work that is capable of generating realistic textures during single image super-resolution. However, the hallucinated details are often accompanied with unpleasant artifacts. To further enhance the visual quality, we thoroughly study three key components of SRGAN - network architecture, adversarial loss and perceptual loss, and improve each of them to derive an Enhanced SRGAN (ESRGAN). In particular, we introduce the Residual-in-Residual Dense Block (RRDB) without batch normalization as the basic network building unit. Moreover, we borrow the idea from relativistic GAN to let the discriminator predict relative realness instead of the absolute value. Finally, we improve the perceptual loss by using the features before activation, which could provide stronger supervision for brightness consistency and texture recovery. Benefiting from these improvements, the proposed ESRGAN achieves consistently better visual quality with more realistic and natural textures than SRGAN and won the first place in the PIRM2018-SR Challenge. The code is available at this https URL .read more
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Boosting High-Level Vision with Joint Compression Artifacts Reduction and Super-Resolution
TL;DR: This paper proposes a context-aware joint CAR and SR neural network (CAJNN) that integrates both local and non-local features to solve CAR andSR in one-stage and demonstrates that CAJNN can serve as an effective image preprocessing method and improve the accuracy for real-scene text recognition and the average precision for tiny face detection.
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Relativistic Approach for Training Self-Supervised Adversarial Depth Prediction Model Using Symmetric Consistency
TL;DR: A novel approach for predicting depth from a single image in a self-supervised manner, specifically using generative adversarial networks (GANs) with enhancements to improve training performance.
Journal ArticleDOI
Polynomial Multiplication in NTRU Prime: Comparison of Optimization Strategies on Cortex-M4
Erdem Alkim,Dean Yun-Li Cheng,Chi-Ming Marvin Chung,Hülya Evkan,Leo Wei-Lun Huang,Vincent Hwang,Ching-Lin Trista Li,Ruben Niederhagen,Cheng-Jhih Shih,Julian Wälde,Bo-Yin Yang +10 more
TL;DR: In this paper, a new approach was introduced to automatically generate high-fidelity synthetic fingerprints at scale, which relies on Generative Adversarial Networks to estimate the probability distribution of human fingerprints and Super-Resolution methods to synthesize fine-grained textures.
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Multi-Resolution Space-Attended Residual Dense Network for Single Image Super-Resolution
TL;DR: A Multi-resolution space-Attended Residual Dense Network (MARDN) to separate low-frequency and high-frequency information for reconstructing high-quality super-resolved images and demonstrates the superiority of the proposed MARDN against the state-of-the-art methods.
Proceedings ArticleDOI
Research on Super-resolution Reconstruction Algorithm of Remote Sensing Image Based on Generative Adversarial Networks
Jiang Wenjie,Luo Xiao-Shu +1 more
TL;DR: This paper studies the image super-resolution reconstruction method for improving the generated anti-network, and shows that the proposed algorithm is better than SRGAN (Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network), SRCNN (Super-Res resolution Convolutional Neural Network) and FSRCNN
References
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Generative Adversarial Nets
Ian Goodfellow,Jean Pouget-Abadie,Mehdi Mirza,Bing Xu,David Warde-Farley,Sherjil Ozair,Aaron Courville,Yoshua Bengio +7 more
TL;DR: A new framework for estimating generative models via an adversarial process, in which two models are simultaneously train: a generative model G that captures the data distribution and a discriminative model D that estimates the probability that a sample came from the training data rather than G.