Work

External Patch-Based Image Restoration Using Importance Sampling

Year: 2019

Type: article

Abstract: This paper introduces a new approach to patchbased 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.

Source: IEEE Transactions on Image Processing

DOI: 10.1109/tip.2019.2912122

Authors: Milad Niknejad, Jose Bioucas-Dias, Mario A. T. Figueiredo

Institutions: University of Lisbon, Instituto de Telecomunicações, Instituto Superior Técnico

Language: English

Topic: Image and Signal Denoising Methods

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funder: Seventh Framework Programme

Awards: G4934273752, G8620237970