scholarly journals Component Divide-and-Conquer for Real-World Image Super-Resolution

2020 ◽  
pp. 101-117
Author(s):  
Pengxu Wei ◽  
Ziwei Xie ◽  
Hannan Lu ◽  
Zongyuan Zhan ◽  
Qixiang Ye ◽  
...  
Keyword(s):  
Real World ◽  
Super Resolution ◽  
Divide And Conquer ◽  
Image Super Resolution ◽  
World Image

scholarly journals Benefiting from Bicubically Down-Sampled Images for Learning Real-World Image Super-Resolution

2021 ◽  
Author(s):  
Mohammad Saeed Rad ◽  
Thomas Yu ◽  
Claudiu Musat ◽  
Hazim Kemal Ekenel ◽  
Behzad Bozorgtabar ◽  
...  
Keyword(s):  
Real World ◽  
Super Resolution ◽  
Image Super Resolution ◽  
World Image

scholarly journals DDet: Dual-Path Dynamic Enhancement Network for Real-World Image Super-Resolution

2020 ◽  
Vol 27 ◽  
pp. 481-485
Author(s):  
Yukai Shi ◽  
Haoyu Zhong ◽  
Zhijing Yang ◽  
Xiaojun Yang ◽  
Liang Lin
Keyword(s):  
Real World ◽  
Super Resolution ◽  
Image Super Resolution ◽  
Dynamic Enhancement ◽  
World Image

GAN-Based Two-Step Pipeline for Real-World Image Super-Resolution

2021 ◽  
pp. 763-772
Author(s):  
Rebati Raman Gaire ◽  
Ronast Subedi ◽  
Ashim Sharma ◽  
Shishir Subedi ◽  
Sharad Kumar Ghimire ◽  
...  
Keyword(s):  
Real World ◽  
Super Resolution ◽  
Image Super Resolution ◽  
World Image

scholarly journals NTIRE 2020 Challenge on Real-World Image Super-Resolution: Methods and Results

2020 ◽  
Author(s):  
Andreas Lugmayr ◽  
Martin Danelljan ◽  
Radu Timofte ◽  
Namhyuk Ahn ◽  
Dongwoon Bai ◽  
...  
Keyword(s):  
Real World ◽  
Super Resolution ◽  
Image Super Resolution ◽  
World Image

scholarly journals AIM 2019 Challenge on Real-World Image Super-Resolution: Methods and Results

2019 ◽  
Author(s):  
Andreas Lugmayr ◽  
Martin Danelljan ◽  
Radu Timofte ◽  
Manuel Fritsche ◽  
Shuhang Gu ◽  
...  
Keyword(s):  
Real World ◽  
Super Resolution ◽  
Image Super Resolution ◽  
World Image

scholarly journals Unsupervised Real-world Image Super Resolution via Domain-distance Aware Training

2021 ◽  
Author(s):  
Yunxuan Wei ◽  
Shuhang Gu ◽  
Yawei Li ◽  
Radu Timofte ◽  
Longcun Jin ◽  
...  
Keyword(s):  
Real World ◽  
Super Resolution ◽  
Image Super Resolution ◽  
World Image

Robust Real-World Image Super-Resolution against Adversarial Attacks

2021 ◽  
Author(s):  
Jiutao Yue ◽  
Haofeng Li ◽  
Pengxu Wei ◽  
Guanbin Li ◽  
Liang Lin
Keyword(s):  
Real World ◽  
Super Resolution ◽  
Image Super Resolution ◽  
World Image

Image super-resolution based on multi-grained cascade forests

2019 ◽  
Vol 17 (04) ◽  
pp. 1950019
Author(s):  
Yaming Wang ◽  
Zhikang Luo ◽  
Weqing Huang ◽  
Yonghua Han
Keyword(s):  
Super Resolution ◽  
Image Features ◽  
Divide And Conquer ◽  
Learning Ability ◽  
Generalization Capability ◽  
Reconstruction Process ◽  
High Quality Image ◽  
Reconstruction Quality ◽  
Image Super Resolution ◽  
The Many

Although neural networks are most commonly used in the field of image super-resolution (SR), methods based on decision trees are still discussed. These kinds of algorithm need less time to compute than others because of their simple structure but still yield high quality image SR. In this paper, we propose an SR algorithm using the multi-grained cascade forest (SRGCF) method. Our algorithm first uses multi-grained scanning to process the spatial relationships of image features, thus the representational learning ability is improved. During the reconstruction process, the image obtained by cascade forest training is used as the input of the next training, therefore, the image features are continuously emphasized. The training of the cascade forest ends when the evaluation value is optimal. Because the decision tree uses a divide-and-conquer strategy, the SR of an image is improved in an iterative manner simply and quickly. Compared with existing methods, our method not only avoids the tradeoff between reconstruction quality and run time, but also has a good generalization capability. It can be quickly applied to the many cases of image SR.

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