Low-Light Image Enhancement Based on Guided Image Filtering in Gradient Domain

Sun, Xiang; Liu, Huijie; Wu, Shiqian; Fang, Zhijun; Li, Chengfan; Yin, Jing

doi:10.1155/2017/9029315

Cited by 12 publications

(6 citation statements)

References 22 publications

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“…As the second image in Figure 8 shows, the outcome of the method in [21] is very bright and looks unnatural, in which the sun is shining at the time the photo is taken. The entropy of the results obtained using the method in [21] shows that it is not as successful in providing information as other [24,26,27] and the CLAHE method in [18,25].…”

Section: Discussionmentioning

confidence: 88%

“…To compare the results of the proposed method with the results of other existing methods, we choose the methods introduced in [18,21,22], and [25][26][27], which are used to enhance color images. In addition to these techniques, the proposed method is compared with CLAHE (Contrast-Limited Adaptive Histogram Equalization) [36] method that has widely been used by other researchers for comparison.…”

Section: Resultsmentioning

confidence: 99%

“…After estimating the illumination by a morphological algorithm, a strategy based on weighting and fusion was used to improve the illumination. Sun et al presented a novel image enhancement approach based on illumination-re ection model [25]. This study applied nonlinear functions for extracting the re ection component, which preserved the important features of the edges of image and improved it.…”

Section: Introductionmentioning

confidence: 99%

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A new image enhancement method considering both dynamic range and color constancy

Hassanpour

Samadiani

2018

Scientia Iranica

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This paper proposes an approach to improving color images su ering from low dynamic range by employing both histogram matching and histogram stretching techniques. In the rst stage, the color image was transformed from RGB into HSV color space, in which the color information was separated from intensity. Then, an appropriate reference image was selected by comparing component V of the enhanced image with component V of the database images using a similarity measure. The selected image was used as the target image in histogram matching algorithm for enhancement. In the second stage, components V and S were linearly stretched in order to recover the image color information. Finally, using the treated V and S components and untreated component of H, the enhanced image was obtained in RGB color space by inverse transform. The qualitative and quantitative results showed that the contrast and color of the resultant image were greatly improved using the proposed method, which outperformed the current state-of-the-art methods.

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Section: Discussionmentioning

confidence: 88%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A new image enhancement method considering both dynamic range and color constancy

Hassanpour

Samadiani

2018

Scientia Iranica

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“…Wang et al [43] put forward a bright-pass filter to estimate the illumination component, which was further optimized by using a relative illumination error function, thus preserving naturalness while enhancing image details. Sun et al [44] proposed to estimate the illumination component using a guided image filter in the gradient domain (GDGIF) and to correct it by using gamma and sigmoid functions, effectively avoiding edge artifacts and enhancing contrast. Nevertheless, noise often exists in low illumination components, and noise amplification tends to occur on enhancement.…”

Section: Related Workmentioning

confidence: 99%

Low and non-uniform illumination color image enhancement using weighted guided image filtering

Wang

Wei

et al. 2021

Comp. Visual Media

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In the state of the art, grayscale image enhancement algorithms are typically adopted for enhancement of RGB color images captured with low or non-uniform illumination. As these methods are applied to each RGB channel independently, imbalanced inter-channel enhancements (color distortion) can often be observed in the resulting images. On the other hand, images with non-uniform illumination enhanced by the retinex algorithm are prone to artifacts such as local blurring, halos, and over-enhancement. To address these problems, an improved RGB color image enhancement method is proposed for images captured under non-uniform illumination or in poor visibility, based on weighted guided image filtering (WGIF). Unlike the conventional retinex algorithm and its variants, WGIF uses a surround function instead of a Gaussian filter to estimate the illumination component; it avoids local blurring and halo artifacts due to its anisotropy and adaptive local regularization. To limit color distortion, RGB images are first converted to HSI (hue, saturation, intensity) color space, where only the intensity channel is enhanced, before being converted back to RGB space by a linear color restoration algorithm. Experimental results show that the proposed method is effective for both RGB color and grayscale images captured under low exposure and non-uniform illumination, with better visual quality and objective evaluation scores than from comparator algorithms. It is also efficient due to use of a linear color restoration algorithm.

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“…Moreover, as an important branch of research in the field of image processing, low-light image enhancement has interdisciplinary and innovative appeal and broad application prospects and has become a focus of interdisciplinary research in recent years [32]. A large number of researchers at home and abroad have been paying increasing attention to this field for quite some time [33]- [38].…”

Section: Introductionmentioning

confidence: 99%

An Experiment-Based Review of Low-Light Image Enhancement Methods

et al. 2020

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Images captured under poor illumination conditions often exhibit characteristics such as low brightness, low contrast, a narrow gray range, and color distortion, as well as considerable noise, which seriously affect the subjective visual effect on human eyes and greatly limit the performance of various machine vision systems. The role of low-light image enhancement is to improve the visual effect of such images for the benefit of subsequent processing. This paper reviews the main techniques of low-light image enhancement developed over the past decades. First, we present a new classification of these algorithms, dividing them into seven categories: gray transformation methods, histogram equalization methods, Retinex methods, frequency-domain methods, image fusion methods, defogging model methods and machine learning methods. Then, all the categories of methods, including subcategories, are introduced in accordance with their principles and characteristics. In addition, various quality evaluation methods for enhanced images are detailed, and comparisons of different algorithms are discussed. Finally, the current research progress is summarized, and future research directions are suggested. INDEX TERMS Review, survey, low-light image enhancement, Retinex method, image enhancement, quality evaluation.

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Low-Light Image Enhancement Based on Guided Image Filtering in Gradient Domain

Cited by 12 publications

References 22 publications

A new image enhancement method considering both dynamic range and color constancy

A new image enhancement method considering both dynamic range and color constancy

Low and non-uniform illumination color image enhancement using weighted guided image filtering

An Experiment-Based Review of Low-Light Image Enhancement Methods

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