论文标题
异步-RED：一种可证明的收敛性异步块平行随机方法，使用深denoising priors
Async-RED: A Provably Convergent Asynchronous Block Parallel Stochastic Method using Deep Denoising Priors
论文作者
论文摘要
通过denoising（Red）正规化是一个最近开发的框架，用于通过将高级DeNoisiser作为图像先验进行整合来解决反问题。最近的工作表明，与预训练的深层Denoisers相结合时，其最先进的表现。但是，当前的红色算法不足以在多核系统上并行处理。我们通过提出一种新的异步红色（异步RED）算法来解决此问题，该算法可以实现异步数据的数据处理，从而使其比其在大规模反问题的串行对应物更快。通过在每次迭代时使用一个随机测量值进一步降低异步红的计算复杂性。我们通过建立对数据保真度和Denoiser的明确假设建立其收敛性来介绍算法的完整理论分析。我们使用预先训练的深层deoisiser作为先验来验证异步恢复图像恢复。
Regularization by denoising (RED) is a recently developed framework for solving inverse problems by integrating advanced denoisers as image priors. Recent work has shown its state-of-the-art performance when combined with pre-trained deep denoisers. However, current RED algorithms are inadequate for parallel processing on multicore systems. We address this issue by proposing a new asynchronous RED (ASYNC-RED) algorithm that enables asynchronous parallel processing of data, making it significantly faster than its serial counterparts for large-scale inverse problems. The computational complexity of ASYNC-RED is further reduced by using a random subset of measurements at every iteration. We present complete theoretical analysis of the algorithm by establishing its convergence under explicit assumptions on the data-fidelity and the denoiser. We validate ASYNC-RED on image recovery using pre-trained deep denoisers as priors.
