.png){kind=icon}
返回
Adaptive Image Resizing Based on Continuous-Domain Stochastic Modeling
.png){kind=icon}
OA
AI
DOI：10.1109/TIP.2013.2285597.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
We introduce an adaptive continuous-domain modeling approach to texture and natural images. The continuous-domain image is assumed to be a smooth function, and we embed it in a parameterized Sobolev space. We point out a link between Sobolev spaces and stochastic auto-regressive models, and exploit it for optimally choosing Sobolev parameters from available pixel values. To this aim, we use exact continuous-to-discrete mapping of the auto-regressive model that is based on symmetric exponential splines. The mapping is computationally efficient, and we exploit it for maximizing an approximated Gaussian likelihood function. We account for non-Gaussian Levy-type processes by deriving a more robust estimator that is based on the sample auto-correlation sequence. Both estimators use multiple initialization values for overcoming the local minima structure of the fitting criteria. Experimental image resizing results indicate that the auto-correlation criterion can cope better with non-Gaussian processes and model mismatch. Our work demonstrates the importance of the auto-correlation function in adaptive image interpolation and image modeling tasks, and we believe it is instrumental in other image processing tasks as well.
Keyword:
Auto-regressive parameter estimation
adaptive interpolation
exponential splines
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:
13.7
论文数:
1.0W
被引数:
8.4W
机构
引用论文
An automated CI/CD process for testing and deployment of Network Applications over 5G infrastructure
OAAI
.png){kind=icon}
OAAI分享收藏Family Businesses in Spain: Tracks Followed and Outcomes Reached by Those Among the Largest Thousand
PREAI
分享收藏
查看更多内容