PAS-MEF: Multi-exposure image fusion based on principal component analysis, adaptive well-exposedness and saliency map
This work addresses image quality enhancement for display devices, but it is incremental as it builds on existing MEF techniques with refinements.
The paper tackles the problem of preserving details in high dynamic range (HDR) imaging for low dynamic range (LDR) screens by proposing a multi-exposure fusion (MEF) approach, resulting in strong statistical and visual outcomes as demonstrated in experiments.
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.