Adaptive image enhancement algorithm based on the model of surface roughness detection system

In view of the relatively high noise interference and halo phenomenon of the traditional adaptive image enhancement algorithm based on the unsharp masking method, a kind of adaptive image enhancement algorithm based on the integration of the model of surface roughness detection system (hereinafter r...

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Bibliographic Details
Published inEURASIP journal on image and video processing Vol. 2018; no. 1; pp. 1 - 9
Main Authors Tian, Jie, Yin, Xijie
Format Journal Article
LanguageEnglish
Published Cham Springer International Publishing 11.10.2018
Springer Nature B.V
SpringerOpen
Subjects
Adaptive algorithms
Adaptive image
Adaptive systems
Algorithms
Amplification
Biometrics
Dynamic range
Engineering
Image compression
Image detection
Image enhancement
Image processing
Image Processing and Computer Vision
Image segmentation
Interference
Masking
Noise reduction
Pattern Recognition
Signal,Image and Speech Processing
Surface roughness
Surface roughness detection
Visual Information Learning and Analytics on Cross-Media Big Data
Adaptive image
Surface roughness detection
Image processing
Image enhancement
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Summary:In view of the relatively high noise interference and halo phenomenon of the traditional adaptive image enhancement algorithm based on the unsharp masking method, a kind of adaptive image enhancement algorithm based on the integration of the model of surface roughness detection system (hereinafter referred to as MSRDS for short) is put forward in this paper. Through the design of the model of the surface roughness detection system, non-linear segmentation, denoising, and adaptive amplification are carried out on the details of the image under this system model. The dynamic range compressed image base layer and the adaptively enhanced image detail layer are non-linearly superimposed to obtain the final enhanced image. Finally, through the comparative experiment analysis, it demonstrates that the method put forward in this paper can suppress the interference noise and the halo phenomenon of the image very well while carrying out dynamic range compression and detail amplification of the adaptive image effectively. And the result thus obtained is very suitable for the back-end image processing of the actual thermal infrared imager.
ISSN:1687-5281
1687-5176
1687-5281
DOI:10.1186/s13640-018-0343-1