External Patch-Based Image Restoration Using Importance Sampling
Abstract
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.
Cite
@article{arxiv.1807.03018,
title = {External Patch-Based Image Restoration Using Importance Sampling},
author = {Milad Niknejad and Jose M. Bioucas-Dias and Mario A. T. Figueiredo},
journal= {arXiv preprint arXiv:1807.03018},
year = {2019}
}