Batch Denoising via Blahut-Arimoto
Qing Li ⋅ Cyril Guyot
Abstract
In this work, we propose to solve batch denoising using Blahut-Arimoto algorithm (BA). Batch denoising via BA (BDBA), similar to Deep Image Prior (DIP), is based on an untrained score-based generative model. Theoretical results show that ourdenoising estimation is highly likely to be close to the best result. Experimentally,we show that BDBA outperforms DIP significantly.
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