Optimal Transport for Unsupervised Denoising Learning
- 点击次数：
- 发表刊物：IEEE Transactions on Pattern Analysis and Machine Intelligence
- 关键字：Image restoration, unsupervised Learning
- 摘要：Recently, much progress has been made in unsupervised denoising learning. However, existing methods more or less rely on some assumptions on the signal and/or degradation model, which limits their practical performance. How to construct an optimal criterion for unsupervised denoising learning without any prior knowledge on the degradation model is still an open question. Toward answering this question, this work proposes a criterion for unsupervised denoising learning based on the optimal transport theory. This criterion has favorable properties, e.g., approximately maximal preservation of the
- 备注：机器学习顶刊，CCF-A（IF: 24）
- 文献类型：期刊论文
- ISSN号：0162-8828
- 是否译文：否
- 发表时间：2022-05-10
- 收录刊物：SCI
- 论文类型：期刊论文
- 发表时间：2022-05-10
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