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External Patch-Based Image Restoration Using Importance Sampling
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DOI：10.1109/TIP.2019.2912122.png)
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摘要
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This paper introduces a new approach to patch-based image restoration based on external datasets and importance sampling. The minimum mean squared error (MMSE) estimate of the image patches, the computation of which requires solving a multidimensional (typically intractable) integral, is approximated using samples from an external dataset. The new method, which can be interpreted as a generalization of the external non-local means, uses self-normalized importance sampling to efficiently approximate the MMSE estimates. The use of self-normalized importance sampling endows the proposed method with great flexibility, namely regarding the statistical properties of the measurement noise. The effectiveness of the proposed method is shown in a series of experiments using both generic large-scale and class-specific external datasets.
Keyword:
Image restoration
image denoising
patch-based methods
non-local means
minimum mean squared error
importance sampling
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期刊
IF:
13.7
论文数:
1.0W
被引数:
8.4W
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Image denoising via sparse and redundant representations over learned dictionaries通过学习字典上的稀疏和冗余表示进行图像去噪
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