Two low illuminance image enhancement algorithms based on grey level mapping

Hong, Cheng; Long, Wei; Li, Yanyan; Liu, Huaguo

doi:10.1007/s11042-020-09919-x

Cited by 11 publications

(6 citation statements)

References 41 publications

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“…Contrast (C): The C index describes the size of the grey‐scale contrast of an image. The larger the value, the clearer the image [32]

\begin{matrix} true \\ right C = \sqrt{\frac{1}{M N}} ({} \sum_{u = 1}^{M} \sum_{v = 1}^{N} I^{2} (u, v) - \frac{1}{M N} {([], \sum_{u = 1}^{M}, \sum_{v = 1}^{N}, I, (u, v))}^{2}) \end{matrix}

where I ( u, v ) represents the pixel grey value at the image coordinate ( u, v ).…”

Section: Resultsmentioning

confidence: 99%

“…In addition, Q ( x ) and Q r ( x ) are the maximum values among R, G and B channels at location x of the enhanced and reference images, respectively. Contrast (C): The C index describes the size of the grey‐scale contrast of an image. The larger the value, the clearer the image [32]

\begin{matrix} true \\ right C = \sqrt{\frac{1}{M N}} ({} \sum_{u = 1}^{M} \sum_{v = 1}^{N} I^{2} (u, v) - \frac{1}{M N} {([], \sum_{u = 1}^{M}, \sum_{v = 1}^{N}, I, (u, v))}^{2}) \end{matrix}

where I ( u, v ) represents the pixel grey value at the image coordinate ( u, v ). Information Entropy (IE): The IE index describes the average amount of information content of the image source and, represents the aggregation characteristics of the image grey distribution. The greater the value, the more the image information and the better the image quality [33]

I E = - \sum_{i} p (i) l o g p (i);

where p ( i ) is the probability that the pixel has a brightness i . Maximum Contrast with Minimum Artefact (MCMA): MCMA indicator is mainly used to detect the side effects of enhancement algorithms.…”

Section: Resultsmentioning

confidence: 99%

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A night low‐illumination image enhancement model based on small probability area filtering and lossless mapping enhancement

Long

Liu

et al. 2021

IET Image Processing

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A novel night-time image enhancement approach was proposed in this paper to address the problems of low contrast and poor details of low-illumination images captured at night. To begin with, the luminance component V was extracted that was irrelevant to the colour information of the image upon converting the image to the HSV space from the RGB space. Then, by converting the luminance component V of the image into the probability space, the image was divided into a small-probability grey-scale area and a normal area based on the theory of probability. Moreover, pixels were transferred from the small probability area to the normal area of the image according to the nearest attribution principle that was established. Lastly, the contrast enhancement of the image was realized thanks to lossless mapping functions without losing the number of grey levels of the image. As can be observed from experimental results, the proposed method is superior to the most advanced algorithm in visual quality and quantitative measurement.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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“…Contrast (C): The C index describes the size of the grey‐scale contrast of an image. The larger the value, the clearer the image [32]

\begin{matrix} true \\ right C = \sqrt{\frac{1}{M N}} ({} \sum_{u = 1}^{M} \sum_{v = 1}^{N} I^{2} (u, v) - \frac{1}{M N} {([], \sum_{u = 1}^{M}, \sum_{v = 1}^{N}, I, (u, v))}^{2}) \end{matrix}

where I ( u, v ) represents the pixel grey value at the image coordinate ( u, v ).…”

Section: Resultsmentioning

confidence: 99%

\begin{matrix} true \\ right C = \sqrt{\frac{1}{M N}} ({} \sum_{u = 1}^{M} \sum_{v = 1}^{N} I^{2} (u, v) - \frac{1}{M N} {([], \sum_{u = 1}^{M}, \sum_{v = 1}^{N}, I, (u, v))}^{2}) \end{matrix}

I E = - \sum_{i} p (i) l o g p (i);

Section: Resultsmentioning

confidence: 99%

A night low‐illumination image enhancement model based on small probability area filtering and lossless mapping enhancement

Long

Liu

et al. 2021

IET Image Processing

Self Cite

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“…Among them, the method based on distribution mapping focuses on the pixel distribution of low light observation, and is committed to using curve transformation, histogram equalization and other means to improve the pixel distribution of the image, so as to improve the brightness and clarity of the image. Histogram equalization [6], [7] and S-shaped curve-based [8], [9] are two representative works of this method. Histogram equalization methods mainly include adjustments from global [10], [11] and local [12], [13].…”

Section: Related Workmentioning

confidence: 99%

A Night Image Enhancement Algorithm Based on MDIFE-Net Curve Estimation

Zhang

Dan

et al. 2023

IEICE Trans. Inf. & Syst.

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In order to solve the low-quality problems such as low brightness, poor contrast, noise interference and color imbalance in night images, a night image enhancement algorithm based on MDIFE-Net curve estimation is presented. This algorithm mainly consists of three parts: Firstly, we design an illumination estimation curve (IEC), which adjusts the pixel level of the low illumination image domain through a non-linear fitting function, maps to the enhanced image domain, and effectively eliminates the effect of illumination loss; Secondly, the DCE-Net is improved, replacing the original Relu activation function with a smoother Mish activation function, so that the parameters can be better updated; Finally, illumination estimation loss function, which combines image attributes with fidelity, is designed to drive the no-reference image enhancement, which preserves more image details while enhancing the night image. The experimental results show that our method can not only effectively improve the image contrast, but also make the details of the target more prominent, improve the visual quality of the image, and make the image achieve a better visual effect. Compared with four existing low illumination image enhancement algorithms, the NIQE and STD evaluation index values are better than other representative algorithms, verify the feasibility and validity of the algorithm, and verify the rationality and necessity of each component design through ablation experiments.

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“…Destruction of the image information in the bright area. (Two low illuminance image enhancement algorithms based on grey level mapping [ 12 ], SDD [ 10 ], RRM [ 11 ], joint enhancement and denoising method via sequential decomposition (JED) [ 14 ], fractional‐order fusion model for low‐light image enhancement [ 15 ], LIME [ 8 ], LECARM [ 9 ]). LECARM, low‐light image enhancement using the camera response model; LIME, low‐light image enhancement via illumination map estimation; RRM, robust Retinex model; SDD, semi‐decoupled decomposition.…”

Section: Introductionmentioning

confidence: 99%

“…Nowadays, there are many enhancement methods for improving the visual quality of low-light images, such as the widely used gamma mapping function, histogram equalization (HE), Gao's low-light image enhancement via illumination map estimation (LIME) [8], Ren's low-light image enhancement using the camera response model (LECARM) [9], Hao's lowlight image enhancement with semi-decoupled decomposition (SSD) [10], Li's structure-revealing low-light image enhancement via the robust Retinex model (RRM) [11], and the latest related methods in the references [12][13][14][15]. Although these methods have different theoretical foundations, they all have two severe flaws.…”

Section: Introductionmentioning

confidence: 99%

Low‐light image enhancement via span correction function and discrete mapping model

Liu

Long

et al. 2023

IET Image Processing

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This paper proposes a new low‐light image enhancement method, which we call the Local Discrete Mapping Method. The new method limits the processing range to small areas with a high information relevance, which can better coordinate the enhancement quality of each area. First, the discrete mapping relationship of pixels (called discrete mapping points) globally occupying a small part of the critical gray value was extracted and designated to keep the enhancement amplitude of each local area consistent. Then, other free mapping points were adjusted according to the local features to achieve the best visual effect in each local area. In addition, this paper also proposes a span correction function that takes the gray span between local pixels as the adjustment object. The function can preserve the gray difference between freely mapped pixels to the maximum and significantly reduce detailed damage in the local area. Finally, we used 1500 test images and eleven objective evaluation indicators in the public dataset to comprehensively test the seven methods. The experimental results showed that the proposed method has an excellent dark area quality enhancement, brightness detail protection, overall noise suppression, and processing speed. It is significantly better than similar methods in terms of visual quality and quantitative testing.

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Two low illuminance image enhancement algorithms based on grey level mapping

Cited by 11 publications

References 41 publications

A night low‐illumination image enhancement model based on small probability area filtering and lossless mapping enhancement

A night low‐illumination image enhancement model based on small probability area filtering and lossless mapping enhancement

A Night Image Enhancement Algorithm Based on MDIFE-Net Curve Estimation

Low‐light image enhancement via span correction function and discrete mapping model

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