Fast exposure fusion of detail enhancement for brightest and darkest regions

Wang, Chunmeng; He, Chen; Xu, Minfeng

doi:10.1007/s00371-021-02079-5

Cited by 14 publications

(2 citation statements)

References 21 publications

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“…Therefore, most methods require some pre-processing, such as histogram equalization, or post-processing of the weight map, such as edge-preserving filtering, to produce a higherquality fusion result. Even though the boundary filtering algorithms were added in some methods [25][26][27] and the halo artifacts can be reduced to some extent, the problem has not been solved at the root. Meanwhile, improvement strategies may bring new issues, such as breaking illumination relationships, over-relying on the bootstrap image, or significantly increasing computational complexity.…”

Section: Pixel-based Methodsmentioning

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: Pixel-based Methodsmentioning

confidence: 99%

Multi-Exposure Image Fusion Techniques: A Comprehensive Review

Liu

Song

et al. 2022

Remote Sensing

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“…Image fusion refers to obtaining different image sequences of the same scene with imaging sensors and combining the details and complementary information of the image sequence to obtain a rich and comprehensive image [11]. Multi-Exposure Fusion (MEF) is one of the branches derived from image fusion; that is, it fuses multiple images with different exposures in the same scene into a new image, preserving the brightest and darkest details in the scene [12]. From the perspective of image decomposition, multi-exposure image fusion can be divided into pixel-based and image block-based fusion methods.…”

Section: Related Workmentioning

confidence: 99%

Multi-Exposure Image Fusion based on Window Segmentation and a Laplacian Pyramid for Chip Package Appearance Quality Detection

Fei¹,

Song²,

Sun³

et al. 2022

IJACSA

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A heterogeneous material image enhancement method based on multi-exposure image fusion is proposed to address the problem of obtaining high-quality images from the single imaging of chips containing two extremely different reflectivity materials. First, a multi-exposure image fusion algorithm based on window segmentation and Laplacian pyramid fusion is proposed. Then, orthogonal experiments are used to optimize the parameters of the imaging system. Next, a method based on information entropy and average gray intensity is utilized to calculate the imaging exposure times of two heterogeneous materials, and two exposure time ranges are obtained that are appropriate for regions with high and low reflectivity. Finally, the subjective and objective experimental evaluations are conducted after the multi-exposure image set has been established. The results show that the fused image has a good visual effect, the information entropy is 6.29, and the average gray intensity is 131.56. In addition, time consumption is reduced by an average of 20.3% compared to the Laplace pyramid strategy. The heterogeneous material enhancement method based on multi-exposure image fusion proposed in this paper is effective and deserving of further research and application.

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