Turbidity-adaptive underwater image enhancement method using image fusion

Han, Bin; Wang, Hao; Luo, Xin; Liang, Chengyuan; Yang, Xuyan; Liu, Shuang; Lin, Yicheng

doi:10.1007/s11465-021-0669-8

Cited by 2 publications

(2 citation statements)

References 43 publications

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“…A gray level probability density function of size M × N with p(r) is the probability of occurrence of the k-th gray level, the formula as (1).…”

Section: Histogram Equalization Principlesmentioning

confidence: 99%

“…The human perception of color is a complex joint physiological and psychological perception process, the naked eye is more sensitive to the brightness of things [1].The pixels in a color image are represented by a combination of multi-channel components, which can be seen as a superposition of different channel images, dot matrix invisible code pictures for red, green, blue (RGB) three-channel pictures, if the three channel components of a RGB model image are done separately to enhance the correction processing, it will lead to hue change or even serious distortion, and the result may be worse [2].…”

Section: Introductionmentioning

confidence: 99%

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An improved γ-CLAHE image enhancement algorithm for dot matrix invisible code

Ren

2023

Eighth International Conference on Electronic Technology and Information Science (ICETIS 2023)

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Dot matrix invisible code is widely used in anti-counterfeiting and traceability of goods, dot matrix invisible code is a kind of technology that "disappears" in the package decoration. This kind of code will neither destroy the overall effect of packaging decoration nor affect the function of barcode, it provide technical support for anti-counterfeit traceability means, which is difficult to be seen by the naked eye and needs to be read under special lighting conditions, merchants and consumers can access product information by identifying the code, it often has the problem of poor contrast due to light intensity, shooting angle and other reasons. The image enhancement technology is used to improve the image quality and lay the foundation for the subsequent work. This paper proposes an improved γ-CLAHE image enhancement algorithm for dot matrix invisible code, which converts the image into the color space with color and brightness separation, performs histogram equalization enhancement processing on the brightness components, combines with Gamma correction, so that the enhanced image quality is significantly improved. In this study, the CLAHE algorithm and the improved algorithms on LAB, HSV and YCrCb color spaces are compared separately, the experimental results show that the improved algorithm is much more effective than the CLAHE algorithm, and the improved algorithm in YCrCb color space is more suitable for image enhancement of dot matrix invisible codes than other color and bright separation color spaces, It has obvious superiority in indicators such as information entropy, mean gradient, standard deviation, etc., and can effectively improve the contrast of low-quality dot matrix invisible codes, at the same time, this study provide ideas for similar invisible code image enhancement.

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“…A gray level probability density function of size M × N with p(r) is the probability of occurrence of the k-th gray level, the formula as (1).…”

Section: Histogram Equalization Principlesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%