图 书 馆
依赖分层β过程的图像插值和去噪Dependent Hierarchical Beta Process for Image Interpolation and Denoising |
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| 课程网址: | http://videolectures.net/aistats2011_zhou_dependent/ |
| 主讲教师: | Mingyuan Zhou |
| 开课单位: | 杜克大学 |
| 开课时间: | 信息不详。欢迎您在右侧留言补充。 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | A dependent hierarchical beta process (dHBP) is developed as a prior for data that may be represented in terms of a sparse set of latent features, with covariate-dependent feature usage. The dHBP is applicable to general covariates and data models, imposing that signals with similar covariates are likely to be manifested in terms of similar features. Coupling the dHBP with the Bernoulli process, and upon marginalizing out the dHBP, the model may be interpreted as a covariate- dependent hierarchical Indian bu et process. As applications, we consider interpolation and denoising of an image, with covariates defi ned by the location of image patches within an image. Two types of noise models are considered: (i) typical white Gaussian noise; and (ii) spiky noise of arbitrary amplitude, distributed uniformly at random. In these examples, the features correspond to the atoms of a dictionary, learned based upon the data under test (without a priori training data). State-of-the-art performance is demonstrated, with efficient inference using hybrid Gibbs, Metropolis-Hastings and slice sampling. |
| 关 键 词: | 分层β过程; 图像插值; 去噪 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2019-10-30：cwx |
| 阅读次数: | 175 |