Details-preserving multi-exposure image fusion based on dual-pyramid using improved exposure evaluation

Wu, Lingfeng; Hu, Junbao; Yuan, Chang; Shao, Zengwu

doi:10.1016/j.rio.2020.100046

Cited by 7 publications

(4 citation statements)

References 44 publications

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“…Regardless, it can be used as an evaluation reference. Huang [1]; Yang [13]; MEF-GAN [17]; Liu [58]; Yang [62]; Martorell [64]; Li [77]; Liu [80]; Chen [81]; Deepfuse [84]; MEFNet [86]; U2fusion [88]; Gao [89]; LXN [123]; Shao [134]; Wu [135]; Merianos [136] The larger, the better 2 Q AB/F Nie [6]; Liu [38]; LST [42]; Hayat [115]; Shao [134] The larger, the better 3 MEF-SSIMc Martorell [64]; UMEF [87]; Shao [134] The larger, the better 4 Mutual information (MI) Nie [6]; Wang [34]; Gao [89]; Choi [137] The larger, the better 5 Peak signal-to-noise ratio (PSNR) Kim [7]; MEF-GAN [17]; Chen [81]; U2fusion [88]; Gao [89]; Shao [134] The larger, the better 6 Natural image quality evaluator (NIQE) Huang [1]; Hayat [115]; Wu [135]; Xu [138] The smaller, the better 7 Standard deviation (SD) MEF-GAN [17]; Gao [89]; Wu [135] The larger, the better 8 Entropy (EN) Gao [89]; Wu…”

Section: Objective Quantitative Comparisonmentioning

confidence: 99%

Multi-Exposure Image Fusion Techniques: A Comprehensive Review

Liu

Song

et al. 2022

Remote Sensing

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Multi-exposure image fusion (MEF) is emerging as a research hotspot in the fields of image processing and computer vision, which can integrate images with multiple exposure levels into a full exposure image of high quality. It is an economical and effective way to improve the dynamic range of the imaging system and has broad application prospects. In recent years, with the further development of image representation theories such as multi-scale analysis and deep learning, significant progress has been achieved in this field. This paper comprehensively investigates the current research status of MEF methods. The relevant theories and key technologies for constructing MEF models are analyzed and categorized. The representative MEF methods in each category are introduced and summarized. Then, based on the multi-exposure image sequences in static and dynamic scenes, we present a comparative study for 18 representative MEF approaches using nine commonly used objective fusion metrics. Finally, the key issues of current MEF research are discussed, and a development trend for future research is put forward.

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Section: Objective Quantitative Comparisonmentioning

confidence: 99%

Multi-Exposure Image Fusion Techniques: A Comprehensive Review

Liu

Song

et al. 2022

Remote Sensing

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“…In recent years, many MEF methods have been successfully developed. According to whether the objects in the input image sequence are moving or not, they were divided into two methods, namely, the static scene MEF method (Mertens et al, 2007 ; Heo et al, 2010 ; Gu et al, 2012 ; Zhang and Cham, 2012 ; Shen et al, 2014 ; Ma and Wang, 2015 ; Nejati et al, 2017 ; Huang et al, 2018 ; Lee et al, 2018 ; Ma et al, 2018 ; Wang et al, 2019 ; Ulucan et al, 2021 ; Wu et al, 2021 ; Hu et al, 2022 ) and the dynamic scene MEF method (Li and Kang, 2012 ; Qin et al, 2014 ; Liu and Wang, 2015 ; Vanmali et al, 2015 ; Fu et al, 2016 ; Ma et al, 2017 ; Zhang et al, 2017 ; Hayat and Imran, 2019 ; Li et al, 2020 ; Qi et al, 2020 ; Jiang et al, 2022 ; Luo et al, 2022 ; Yin et al, 2022 ). Mertens et al ( 2007 ) proposed a technique for fusing exposure sequences into high-quality images using multi-scale resolution.…”

Section: Introductionmentioning

confidence: 99%

“…Simple detail components are used to strengthen the fused image details, which can retain the brightest and darkest area details in the HDR scene. A few pieces of literature (Ulucan et al, 2021 ; Wu et al, 2021 ; Hu et al, 2022 ) describe the recent results of the MEF method. Ulucan et al ( 2021 ) designed a MEF technology to obtain accurate weights of fused images.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Multi-exposure electric power monitoring image fusion method without ghosting based on exposure fusion framework and color dissimilarity feature

Chen¹,

Li²,

Shen³

et al. 2023

Front. Neurorobot.

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To solve the ghosting artifacts problem in dynamic scene multi-scale exposure fusion, an improved multi-exposure fusion method has been proposed without ghosting based on the exposure fusion framework and the color dissimilarity feature of this study. This fusion method can be further applied to power system monitoring and unmanned aerial vehicle monitoring. In this study, first, an improved exposure fusion framework based on the camera response model was applied to preprocess the input image sequence. Second, the initial weight map was estimated by multiplying four weight items. In removing the ghosting weight term, an improved color dissimilarity feature was used to detect the object motion features in dynamic scenes. Finally, the improved pyramid model as adopted to retain detailed information about the poor exposure areas. Experimental results indicated that the proposed method improves the performance of images in terms of sharpness, detail processing, and ghosting artifacts removal and is superior to the five existing multi-exposure image fusion (MEF) methods in quality evaluation.

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