A novel deghosting method for exposure fusion

Wang, Chunmeng; He, Chen

doi:10.1007/s11042-018-6261-5

Cited by 4 publications

(2 citation statements)

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“…The constraints on moving objects were incorporated into the framework, which consisted of sparsity, connectivity, and the a priori information from underexposed and overexposed areas. The study in [110] presented a ghost-free MEF method based on an improved difference approach. Before becoming ghost-free, each input image was normalized to the brightness consistent with the reference image's exposure level.…”

Section: Moving Object Selection or Registrationmentioning

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: Moving Object Selection or Registrationmentioning

confidence: 99%

Multi-Exposure Image Fusion Techniques: A Comprehensive Review

Liu

Song

et al. 2022

Remote Sensing

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“…Since the moving region only came from a single image, some translucent areas may be still introduced. Wang and He 16 proposed a deghosting MEF algorithm based on an improved difference strategy. Before detecting ghosts, the exposure of each source image was normalized to the brightness consistent with the reference image.…”

Section: Introductionmentioning

confidence: 99%

Multi-exposure image fusion of dynamic scenes using consistency detection and weighted least squares filtering

Liu

Wang

et al. 2023

Opt. Eng.

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.The high dynamic range image obtained by multi-exposure image fusion (MEF) contains more detailed information, which has broad application prospects and crucial practical significance in many fields. However, due to the introduction of the ghosting artifacts caused by object movement and camera shake, MEF in the dynamic scenes has always been a challenge. To address this problem, we designed a deghosting method for MEF. The over- and under-exposed images are corrected by intensity mapping and consistency detection to obtain the aligned latent images. Then the high- and low-frequency components of the latent images are generated using weighted least squares filtering. The blending weights are calculated based on the image luminance and the exposedness function. These frequency components are integrated into the final deghosted image with more texture details and vivid color. A comprehensive evaluation experiment is carried out, proving that the proposed method has a better visual effect and stable performance than the state-of-the-art deghosting MEF methods.

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Strong ghost removal in multi-exposure image fusion using hole-filling with exposure congruency

Shao

Jiang

et al. 2021

Journal of Visual Communication and Image Representation

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A novel deghosting method for exposure fusion

Cited by 4 publications

References 20 publications

Multi-Exposure Image Fusion Techniques: A Comprehensive Review

Multi-Exposure Image Fusion Techniques: A Comprehensive Review

Multi-exposure image fusion of dynamic scenes using consistency detection and weighted least squares filtering

Strong ghost removal in multi-exposure image fusion using hole-filling with exposure congruency

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