Low light image enhancement based on two-step noise suppression

Su, Haonan; Jung, Cheolkon

doi:10.1109/icassp.2017.7952502

Cited by 24 publications

(15 citation statements)

References 13 publications

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“…In the methods, a shrinkage function is used for preventing the noise amplification. Low light image enhancement based on two-step noise suppression (LLIE) [4] uses both noise level function (NLF) and just noticeable difference (JND) for contrast enhancement with noise suppression. Although this method can reduce some noise, it does not preserve details in bright areas as with histogram-based methods.…”

Section: B Image Enhancementmentioning

confidence: 99%

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A Noise-aware Enhancement Method for Underexposed Images

Chien

Kinoshita

Kiya

2019

2019 IEEE International Conference on Consumer Electronics - Asia (ICCE-Asia)

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A novel method of contrast enhancement is proposed for underexposed images, in which heavy noise is hidden. 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 these reasons, various contrast enhancement methods have been proposed so far. These methods, however, have two problems: (1) The loss of details in bright regions due to over-enhancement of contrast.(2) The noise is amplified in dark regions because conventional enhancement methods do not consider noise included in images. The proposed method aims to overcome these problems. In the proposed method, a shadow-up function is applied to adaptive gamma correction with weighting distribution, and a denoising filter is also used to avoid noise being amplified in dark regions. As a result, the proposed method allows us not only to enhance contrast of dark regions, but also to avoid amplifying noise, even under strong noise environments.

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Section: B Image Enhancementmentioning

confidence: 99%

“…To avoid the noise amplification problem, some contrast enhancement methods have been proposed [1], [2], [4]. However, they do not preserve details in bright areas, although, they can reduce some noise.…”

Section: Introductionmentioning

confidence: 99%

A Noise-aware Enhancement Method for Underexposed Images

Chien

Kinoshita

Kiya

2019

2019 IEEE International Conference on Consumer Electronics - Asia (ICCE-Asia)

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“…The following nine methods were compared in this paper: HE, contrast limited adaptive histograph equalization (CLAHE) [1], adaptive gamma correction with weighting distribution (AGCWD) [2], contrast-accumulated HE (CACHE) [3], low light image enhancement based on two-step noise suppression (LLIE) [6], low-light image enhancement via illumination map estimation (LIME) [4], simultaneous reflectance and illumination estimation (SRIE) [5], bio-inspired ME fusion framework for low-light image enhancement (BIMEF) [21], and the proposed method. For the proposed method, Nejati's method [26] was used as a fusion function .…”

Section: Simulationmentioning

confidence: 99%

“…However, HE- and Retinex-based methods often cause details to be lost in bright areas in images, i.e., over-enhancement. To avoid over-enhancement, numerous image enhancement methods have been developed [1–3,6]. However, these methods cannot sufficiently enhance the contrast in dark regions due to the difficulty of using detailed local features.…”

Section: Introductionmentioning

confidence: 99%

“…Various kinds of research on single-image enhancement have so far been reported [1][2][3][4][5]. Some researchers [6][7][8] have also studied joint methods that make it possible not only to enhance images but also to reduce noise. Among methods for enhancing images, histogram equalization (HE) has received the most attention because of its intuitive implementation quality and high efficiency.…”

Section: Introductionmentioning

confidence: 99%

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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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A Novel Design and Implementation of Imaging Chip Using AXI Protocol for MPSOC on FPGA

Archana¹,

Patel²

2018

Intelligent Systems in Cybernetics and Automation Control Theory

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Low light image enhancement based on two-step noise suppression

Cited by 24 publications

References 13 publications

A Noise-aware Enhancement Method for Underexposed Images

A Noise-aware Enhancement Method for Underexposed Images

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

A Novel Design and Implementation of Imaging Chip Using AXI Protocol for MPSOC on FPGA

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