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

TL;DR

This paper introduces PAS-MEF, a multi-exposure image fusion method that combines principal component analysis, adaptive well-exposedness, and saliency maps to produce high-quality HDR-like images from LDR inputs, outperforming existing techniques.

Contribution

It presents a novel multi-exposure fusion approach using a simple weight extraction method based on PCA, well-exposedness, and saliency maps, with effective refinement and fusion strategies.

Findings

Produces superior visual quality compared to existing methods.

Demonstrates strong statistical performance in experiments.

Efficient and effective fusion process for HDR-like images.

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.