Automatic Exposure Compensation for Multi-Exposure Image Fusion

Kinoshita, Yuma; Shiota, Sayaka; Kiya, Hitoshi

doi:10.1109/icip.2018.8451401

Cited by 17 publications

(7 citation statements)

References 24 publications

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“…Meanwhile, multi-image-based image enhancement methods, referred to as multi-exposure image fusion (MEF) methods, have been developed [10][11][12][13][14][15][16][17][18]. MEF methods utilize a stack of differently exposed images, called multiexposure (ME) images, and fuse them to produce an image with high quality.…”

Section: Corresponding Authormentioning

confidence: 99%

Automatic exposure compensation using an image segmentation method for single-image-based multi-exposure fusion

Kinoshita

Kiya

2018

SIP

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In this paper, an automatic exposure compensation method is proposed for image enhancement. For the exposure compensation, a novel image segmentation method based on luminance distribution is also proposed. Most single-image-enhancement methods often cause details to be lost in bright areas in images or cannot sufficiently enhance contrasts in dark regions. The image-enhancement method that uses the proposed compensation method enables us to produce high-quality images which well represent both bright and dark areas by fusing pseudo multi-exposure images generated from a single image. Here, pseudo multi-exposure images are automatically generated by the proposed exposure compensation method. To generate effective pseudo multi-exposure images, the proposed segmentation method is utilized for automatic parameter setting in the compensation method. In experiments, image enhancement with the proposed compensation method outperforms state-of-the-art image enhancement methods including Retinex-based methods, in terms of both entropy and statistical naturalness. Moreover, visual comparison results show that the proposed compensation method is effective in producing images that clearly present both bright and dark areas.

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Section: Corresponding Authormentioning

confidence: 99%

Automatic exposure compensation using an image segmentation method for single-image-based multi-exposure fusion

Kinoshita

Kiya

2018

SIP

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“…Another way for enhancing images is to use multi-exposure image fusion, by using photos with different exposures. 16,17 In this paper,we utilize the illumination layer in Retinex theory to reduce the effects of noise and use histogram-based AGCWD to adjust the illumination layer. We consider not only the noise but also the highlights.…”

Section: Image Enhancementmentioning

confidence: 99%

A Retinex-based Image Enhancement Scheme with Noise Aware Shadow-up Function

Chien¹,

Kinoshita²,

Shiota³

et al. 2018

Preprint

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This paper proposes a novel image contrast enhancement method based on both a noise aware shadow-up function and Retinex (retina and cortex) decomposition. Under low light conditions, images taken by digital cameras have low contrast in dark or bright regions. This is due to a limited dynamic range that imaging sensors have. For this reason, various contrast enhancement methods have been proposed. Our proposed method can enhance the contrast of images without not only over-enhancement but also noise amplification. In the proposed method, an image is decomposed into illumination layer and reflectance layer based on the retinex theory, and lightness information of the illumination layer is adjusted. A shadow-up function is used for preventing over-enhancement. The proposed mapping function, designed by using a noise aware histogram, allows not only to enhance contrast of dark region, but also to avoid amplifying noise, even under strong noise environments.

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“…This is because colors other than white are not considered in designing the matrix [10]. For this reason, in some computer vision tasks such as detecting objects with specific colors, the specific colors still suffer from lighting effects [11]- [17].…”

Section: Introductionmentioning

confidence: 99%

Multi-color balancing for correctly adjusting the intensity of target colors

Akazawa

Kinoshita

Kiya

2021

2021 IEEE 3rd Global Conference on Life Sciences and Technologies (LifeTech)

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In this paper, we propose a novel multi-color balance method for reducing color distortions caused by lighting effects. The proposed method allows us to adjust three target-colors chosen by a user in an input image so that each target color is the same as the corresponding destination (benchmark) one. In contrast, white balancing is a typical technique for reducing the color distortions, however, they cannot remove lighting effects on colors other than white. In an experiment, the proposed method is demonstrated to be able to remove lighting effects on selected three colors, and is compared with existing white balance adjustments.

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Automatic Exposure Compensation for Multi-Exposure Image Fusion

Cited by 17 publications

References 24 publications

Automatic exposure compensation using an image segmentation method for single-image-based multi-exposure fusion

Automatic exposure compensation using an image segmentation method for single-image-based multi-exposure fusion

A Retinex-based Image Enhancement Scheme with Noise Aware Shadow-up Function

Multi-color balancing for correctly adjusting the intensity of target colors

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