PAS-MEF: Multi-exposure image fusion based on principal component analysis, adaptive well-exposedness and saliency map

• URL: http://arxiv.org/abs/2105.11809v1
• Date: Tue, 25 May 2021 10:22:43 GMT
• Title: PAS-MEF: Multi-exposure image fusion based on principal component analysis, adaptive well-exposedness and saliency map
• Authors: Diclehan Karakaya, Oguzhan Ulucan, Mehmet Turkan
• Abstract summary: With regular low dynamic range (LDR) capture/display devices, significant details may not be preserved in images due to the huge dynamic range of natural scenes. This study proposes an efficient multi-exposure fusion (MEF) approach with a simple yet effective weight extraction method. Experimental comparisons with existing techniques demonstrate that the proposed method produces very strong statistical and visual results.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: High dynamic range (HDR) imaging enables to immortalize natural scenes similar to the way that they are perceived by human observers. With regular low dynamic range (LDR) capture/display devices, significant details may not be preserved in images due to the huge dynamic range of natural scenes. To minimize the information loss and produce high quality HDR-like images for LDR screens, this study proposes an efficient multi-exposure fusion (MEF) approach with a simple yet effective weight extraction method relying on principal component analysis, adaptive well-exposedness and saliency maps. These weight maps are later refined through a guided filter and the fusion is carried out by employing a pyramidal decomposition. Experimental comparisons with existing techniques demonstrate that the proposed method produces very strong statistical and visual results.

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