Multi-Exposure Image Fusion Based on Linear Embeddings and Watershed Masking

Abstract

High dynamic range imaging (HDRI) is a challenging technology but yet demanding for modern imaging applications. Low-cost image sensors have limited dynamic range, and it is not always possible to capture and display natural scenes with high contrast and information loss in any exposure is inevitable. Three solutions for HDRI are using expensive high dynamic range (HDR) cameras with HDR-compatible displays, tone mapping operators for low dynamic range (LDR) screens, and capturing and fusing multiple exposures of the same LDR scene via image fusion algorithms. Companies that produce user grade devices prefer multi-exposure fusion (MEF) approaches to obtain HDR-like images for LDR screens due to its low cost. Hence, merging a stack of images containing different exposures of the same scene into a single informative image is an attractive research field. In this study, a novel, simple yet effective method is proposed for static image exposure fusion. The developed technique is based on weight map extraction via linear embeddings and watershed masking. The main advantage lies in watershed masking-based adjustment for obtaining accurate weights for image fusion. The comprehensive experimental comparisons demonstrate very strong visual and statistical results, and this approach should facilitate future MEF studies. (C) 2020 Elsevier B.V. All rights reserved.