Deep gradual flash fusion for low-light enhancement

Kim, Jae Woo; Ryu, Je Ho; Kim, Jong Ok

doi:10.1016/j.jvcir.2020.102903

Cited by 5 publications

(3 citation statements)

References 25 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%

“…1 HDR-VDP-2 Kim [7]; Kara duzović-Hadžiabdić [19]; Tursun [120]; Yan [139] The larger, the better 2 MEF-SSIMd Shao [134] The larger, the better…”

Section: No Metric References Remarksmentioning

confidence: 99%

“…The main difference between these image fusion tasks is that the source images are different, and the source images of MEF are a series of images with different exposure levels. In addition, it can also be used for image enhancement under low illumination [6,7], defogging [8], and saliency detection [9] by fusing or generating pseudo exposure sequences.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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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