.png){kind=icon}
返回
Variational probabilistic generative framework for single image super-resolution
.png){kind=icon}
PRE
AI
DOI：10.1016/j.sigpro.2018.10.004.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
In this paper, a general variational probabilistic generative framework parameterized by deep networks is proposed for single image super-resolution, which assembles the advantages of coding-based methods and regression-based methods. We use probabilistic generative networks to model the joint full likelihood of a pair of low-resolution (LR) and high-resolution (HR) patches which are generated from a shared latent representation. An inference model is applied to infer the stochastic distribution of the latent representation. By jointly optimizing the generative and inference models, a regression process to the distribution of the HR patch is implied during the learning phase, which provides an efficient forward mapping to accomplish the super-resolution task. We use our framework as a guidance and develop a new model called PGM-CP, with the help of an informative conditional prior and a consistent recognition model. We likewise show how three existing popular example-based SR methods can be reinvented under our framework. The effectiveness and efficiency of the proposed method is examined based on three public datasets. Experimental results demonstrate that our model is competitive with state-of-the-art approaches, especially when the image is corrupted by noise. (C) 2018 Elsevier B.V. All rights reserved.
Keyword:
Probabilistic generative model
Image super-resolution
Conditional prior
Recognition model
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
3.6
论文数:
9.9K
被引数:
1.7W
机构
引用论文
Improved Particle Size Control for the Dispersion Polymerization of Methyl methacrylate in Supercritical Carbon Dioxide超临界二氧化碳中甲基丙烯酸甲酯分散聚合的改进粒度控制
OAAI
OAAI分享收藏分享收藏Deep learning for automatic stereotypical motor movement detection using wearable sensors in autism spectrum disorders在自闭症谱系障碍中使用可穿戴传感器进行自动定型运动运动检测的深度学习.png)
SIGNAL PROCESSINGOAAI
SIGNAL PROCESSINGIF3.6
OAAI分享收藏Behaviour of Prunus cultivars and hybrids towards Agrobacterium tumefaciens estimated from hardwood cuttingsAgronomie
OAAI
AgronomieIF0
OAAI分享收藏
查看更多内容