Extended Exposure Fusion

Abstract

Extended Exposure Fusion (EEF) is a high dynamic range imaging technique. It was recently proposed as an improvement of an earlier technique called Exposure Fusion (EF), itself widely used thanks to its very good results. But EF has two well-known artifacts: an out-of-range artifact and a low-frequency halo. The extended version solves them, and delivers fused results with enhanced contrast everywhere in the image. We give in this paper a precise description, analysis and implementation of the extended exposure fusion. We notably verify that the artifacts are removed and that the local contrast is improved compared to the original exposure fusion. Source Code The Matlab/Octave source code, the code documentation, and the online demo are accessible at the web page of this article 1. Usage instructions are included in the README.txt file of the archive.

Efficient joint noise removal and multi exposure fusion

“…Li et al [29] fusion is over-smoothed, while the result by Hayat et al [16] is over-saturated at bright parts. The result of [40], Hessel et al [18], Zhang et al [66] and ours are quite similar.…”

“…Mertens et al [41] proposed a pixel wise combination of images depending on contrast, saturation and well-exposedness. Many algorithms appeared improving aspects of their method or making it motion adaptive, such as Li et al [31], An et al [3], Ocampo et al [44], Liu et al [35], Hayat et al [16] and Hessel et al [18].…”

Efficient joint noise removal and multi exposure fusion

“…In this section we compare the proposed method with state of the art algorithms for exposure fusion. We compare with Mertens et al [ 1 ], Ma et al [ 5 ], Li et al [ 44 ], Kou et al [ 28 ], Ma et al [ 55 ], Hessel et al, (EEF) [ 19 ], Xu et al [ 35 ], Zhang et al (IFCNN) [ 64 ], Li et al (CNNFEAT) [ 63 ], Hayat et al (MEF-Sift) [ 22 ] and Martorell et al [ 2 ]. The results from Ma et al [ 5 ] and Ma et al [ 55 ] were computed with the software downloaded from the corresponding author’s webpage.…”

“…The results of Mertens et al [ 1 ] were obtained from the dataset provided in [ 56 , 72 ]. The code for Hessel et al (EEF) [ 19 , 73 ], Xu et al (FusionDN) [ 35 , 74 ], Zhang et al(IFCNN) [ 64 , 75 ], Li et al (CNNFEAT) [ 63 , 76 ] and Hayat et al (MEF-Sift) [ 22 , 77 ] were obtained from the corresponding GitHub webpages. The code by Xu et al, originally proposed to fuse only pairs of images, has been adapted to fuse any sequence.…”

“…Mertens et al [ 1 ] proposed to combine the images by averaging, choosing for each pixel a different weight depending on saturation, contrast and well-exposedness. Since such an average produces ghosting effect if the images are not well aligned, several methods were proposed to take motion into account, such as An et al [ 17 ], Liu et al [ 18 ], Hessel et al [ 19 ] Li et al [ 20 ], Ocampo et al [ 21 ] and Hayat et al [ 22 ].…”