Pas-Mef: Multi-Exposure Image Fusion Based On Principal Component Analysis, Adaptive Well-Exposedness And Saliency Map
TL
TL;DR
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.
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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Fast Multi-Scale Structural Patch Decomposition for Multi-Exposure Image Fusion
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Multiple-Exposure Image Fusion for HDR Image Synthesis Using Learned Analysis Transformations
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