Efficient joint noise removal and multi exposure fusion

Antoni Buades, Jose Luis Lisani and Onofre Martorell

PLOS ONE, 2022, vol. 17, issue 3, 1-19

Abstract: Multi-exposure fusion (MEF) is a technique that combines different snapshots of the same scene, captured with different exposure times, into a single image. This combination process (also known as fusion) is performed in such a way that the parts with better exposure of each input image have a stronger influence. Therefore, in the result image all areas are well exposed. In this paper, we propose a new method that performs MEF and noise removal. Rather than denoising each input image individually and then fusing the obtained results, the proposed strategy jointly performs fusion and denoising in the Discrete Cosinus Transform (DCT) domain, which leads to a very efficient algorithm. The method takes advantage of spatio-temporal patch selection and collaborative 3D thresholding. Several experiments show that the obtained results are significantly superior to the existing state of the art.

Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0265464

DOI: 10.1371/journal.pone.0265464

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