2014
DOI: 10.1002/col.21931
|View full text |Cite
|
Sign up to set email alerts
|

Fast image enhancement based on color space fusion

Abstract: The current Retinex algorithm processes the RGB channels separately for color image enhancement. However, it changes the ratios of RGB components and also causes some serious problems, such as color distortion, color noise, and the halo artifacts. To solve these issues, we propose a novel algorithm based on color space fusion. The single scale Retinex with fast mean filtering is applied to the luminance component in hue‐saturation‐value (HSV) color space. An enhancement adjustment factor is introduced to avoid… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
3
1
1

Citation Types

0
14
0

Year Published

2017
2017
2022
2022

Publication Types

Select...
7
2

Relationship

0
9

Authors

Journals

citations
Cited by 21 publications
(14 citation statements)
references
References 28 publications
0
14
0
Order By: Relevance
“…RGB color values are clustered together based on their discriminative power in a classification problem [52], so that each cluster has the explicit objective to minimize the decline of mutual information of the final representation. Besides, this kind of color description can automatically maintain photometric invariance to some extent.…”
Section: Feature Extractionmentioning
confidence: 99%
“…RGB color values are clustered together based on their discriminative power in a classification problem [52], so that each cluster has the explicit objective to minimize the decline of mutual information of the final representation. Besides, this kind of color description can automatically maintain photometric invariance to some extent.…”
Section: Feature Extractionmentioning
confidence: 99%
“…Li et al [18] proposed the WGIF, where a spatially varying gradient weight constraint 2 ( ) = /Γ was added in (7).…”
Section: Weighted Guided Image Filtermentioning
confidence: 99%
“…The commonly used methods include dark channel prior model [1,2], neural network model [3,4], histogram equalization (HE) [5,6], image fusion [7,8], wavelet domain algorithm [9,10], and illuminationreflection model [11][12][13][14]. It is noted that the adaptability of dark channel prior model is poor in disposing the images with rich details and high brightness [2].…”
Section: Introductionmentioning
confidence: 99%
“…Xie et al [14] used guided filter to improve SSR and got good results in enhancing the image quality and finger vein recognition accuracy. Xiao et al [16] employed a fast mean filtering to improve the performance of SSR. Fu et al [17] tried simultaneous estimation of illumination and reflectance in the linear domain to improve Retinex, and their method was proved to be better than other common methods.…”
Section: Introductionmentioning
confidence: 99%