Nighttime low illumination image enhancement with single image using bright/dark channel prior

Shi, Zhongchao; Zhu, Mei mei; Guo, Bin; Zhao, Minghua; Zhang, Changqing

doi:10.1186/s13640-018-0251-4

Cited by 55 publications

(40 citation statements)

References 25 publications

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“…Interests regarding image preprocessing, including image de-noising [5] and image enhancement [6], especially on ancient Chinese calligraphy image enhancement [1][2][3][4][7][8][9][10] have seen increasing in recent years; for instance, Zheng et.al. [1] presented a de-noising method for stele images using guided filter on the L channel.…”

Section: Related Workmentioning

confidence: 99%

“…where H(| α x (x, y)| + | α y (x, y)| ) is a binary function returning 1 when |α x (x, y) | + | α y (x, y) | ≠ 0; otherwise, it returns 0. By alternatively computing (5,6), we obtain the final base image B(x,y) and its corresponding detail image D(x,y). Because all of the random-noise remains in the detail image D(x,y), we take the final base image B(x,y) as the random-noise free map.…”

Section: Random-noise Free Map Computation With L0 Gradient Minimizationmentioning

confidence: 99%

See 1 more Smart Citation

A de-noising method based on L0 gradient minimization and guided filter for ancient Chinese calligraphy works on steles

2019

J Image Video Proc.

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A clear stele image of ancient Chinese calligraphy pieces is very useful for studying ancient Chinese calligraphy. However, due to hundreds of or even thousands of years of natural or artificial damage on stele, images of ancient Chinese stele calligraphy works usually suffer from a large amount of image noise, and which usually leads to a poor visibility. To address this problem, in this paper, we propose a de-noising method based on L0 gradient minimization and guided filter. It consists of two main operations in sequence: First, L0 gradient minimization is utilized to obtain a random-noise free map, and then the random-noise free map is used as a guided image, and convoluted with its corresponding original noised stele image by a guided filter to obtain an edge preserved random-noise free image. Finally, the eight-connection region-based de-noising technique is followed to remove ant-like isolated blocks. Experiments demonstrate that the proposed method is superior to several recent published stele image de-noising techniques in terms of preserving the character structures.

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Section: Related Workmentioning

confidence: 99%

Section: Random-noise Free Map Computation With L0 Gradient Minimizationmentioning

confidence: 99%

A de-noising method based on L0 gradient minimization and guided filter for ancient Chinese calligraphy works on steles

2019

J Image Video Proc.

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“…At present, video image enhancement algorithms can be divided into machine learning and non-machine learning. Using machine learning to enhance video images can usually achieve good results in large data sets, but this method also has its own shortcomings [1][2]. First, it needs a lot of data to train.…”

Section: Introductionmentioning

confidence: 99%

Low Illumination Video Image Enhancement

Yang

Kasabov

2020

IEEE Photonics J.

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Due to weather conditions, brightness conditions, capture equipment and other factors, leads to video unclear or even abnormally confused, which is not conducive to monitoring, and can not meet the needs of applications. Based on the actual data of night video surveillance, this paper proposes a new low illumination video image enhancement algorithm, which overcomes the existing problems. We analyze the characteristics of low illumination video image, and use HSV color space instead of traditional RGB space to enhance the robustness of video contrast and color distortion. At the same time, we use wavelet image fusion to highlight the details of video image, so the enhanced video has higher clarity and visual effect. Compared with other four algorithms, the proposed algorithm outperforms the above algorithms in subjective evaluation and objective evaluation. At the same time, compared with other algorithms, the proposed algorithm has faster processing time for each frame. Experiments show that the algorithm can effectively improve the overall brightness and contrast of video images, and avoid the over-enhancement of bright areas near the light source, which can meet the practical application requirements of video surveillance.

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“…Pre-processing in face recognition systems involves enhancing an input face image in order to improve its quality by making more facial features in the image visible. Pre-processing enhances the performance of face recognition techniques [1,2]. Further, the pre-processing stage amends distorted images and acquires regions of interest in an image for onward feature extraction.…”

Section: Introductionmentioning

confidence: 99%

“…Reddy's method uses three variants of the occupied bin space to enhance the low-contrasted dark, bright, and gray images. Shi et al [2] presented a dual channel prior-based method for nighttime low illumination image enhancement using a single image that is based on two existing image priors i.e., bright and dark priors. They used the bright channel prior to obtain the initial transmission estimate and used the dark prior as a complementary channel to adjust any wrong transmission estimate produced by the bright channel prior.…”

Section: Introductionmentioning

confidence: 99%

A new evaluation function for face image enhancement in unconstrained environments using metaheuristic algorithms

Oloyede

Hancke

Myburgh

et al. 2019

J Image Video Proc.

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Image enhancement is an integral component of face recognition systems and other image processing tasks such as in medical and satellite imaging. Among a number of existing image enhancement methods, metaheuristic-based approaches have gained popularity owing to their highly effective performance rates. However, the need for improved evaluation functions is a major research concern in the study of metaheuristic-based image enhancement methods. Thus, in this paper, we present a new evaluation function for improving the performance of metaheuristic-based image enhancement methods. Essentially, we applied our new evaluation function in conjunction with metaheuristic-based optimization algorithms in order to select automatically the best enhanced face image based on a linear combination of different key quantitative measures. Furthermore, different from other existing evaluation functions, our evaluation function is finitely bounded to determine easily whether an image is either too dark or too bright. This makes it better suited to find optimal solutions (best enhanced images) during the search process. Our method was compared with existing metaheuristic-based methods and other state-of-the-art image enhancement techniques. Based on the qualitative and quantitative measures obtained, our approach is shown to enhance facial images in unconstrained environments significantly.

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Nighttime low illumination image enhancement with single image using bright/dark channel prior

Cited by 55 publications

References 25 publications

A de-noising method based on L0 gradient minimization and guided filter for ancient Chinese calligraphy works on steles

A de-noising method based on L0 gradient minimization and guided filter for ancient Chinese calligraphy works on steles

Low Illumination Video Image Enhancement

A new evaluation function for face image enhancement in unconstrained environments using metaheuristic algorithms

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