External Patch-Based Image Restoration Using Importance Sampling
This provides a flexible method for image restoration tasks, though it appears incremental as it generalizes existing non-local means approaches.
The paper tackles image restoration by approximating Minimum Mean Squared Error estimates using external datasets and importance sampling, achieving effective results in experiments with generic and class-specific datasets.
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 (NLM), 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.