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Low-Rank Matrix Recovery from Noise via an MDL Framework-based Atomic Norm
The recovery of the underlying low-rank structure of clean data corrupted with sparse noise/outliers is attracting increasing interest.
Learning a Deep Part-based Representation by Preserving Data Distribution
Then one hopes to learn a new low dimensional representation which can preserve the intrinsic structure embedded in the original high dimensional data space perfectly.
A Multiscale Image Denoising Algorithm Based On Dilated Residual Convolution Network
To address this issue, here we propose a novel deep residual learning model that combines the dilated residual convolution and multi-scale convolution groups.
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