Multi-scale retinex image enhancement algorithm based on fabric defect database

2020 ◽  
Author(s):  
Wang Huang ◽  
DUAN FAJIE ◽  
ZHOU WEITI
Keyword(s):  
Image Enhancement ◽  
Multi Scale ◽  
Fabric Defect ◽  
Enhancement Algorithm

Improved multi-scale retinex image enhancement algorithm

2010 ◽  
Vol 30 (8) ◽  
pp. 2091-2093 ◽  
Author(s):  
Xiao-ming WANG ◽  
Chang HUANG ◽  
Quan-bin LI ◽  
Jin-gao LIU
Keyword(s):  
Image Enhancement ◽  
Multi Scale ◽  
Enhancement Algorithm

An improved infrared image enhancement algorithm based on multi-scale decomposition

2014 ◽  
Author(s):  
Honghui Zhang ◽  
Haibo Luo ◽  
Xin-rong Yu ◽  
Qing-hai Ding
Keyword(s):  
Image Enhancement ◽  
Infrared Image ◽  
Multi Scale ◽  
Enhancement Algorithm

Improved Multi Scale DR Medical Image Enhancement Algorithm

2014 ◽  
Vol 530-531 ◽  
pp. 413-417
Author(s):  
Xiao Jing Sun ◽  
Ai Bin Chen
Keyword(s):  
Image Enhancement ◽  
Low Frequency ◽  
Frequency Component ◽  
Image Feature ◽  
High Frequency Component ◽  
Image Processing Algorithm ◽  
Multi Scale ◽  
Gaussian Pyramid ◽  
Layer Image ◽  
Enhancement Algorithm

The original DR image is decomposed into different scale and frequency of the band image sequence by using Laplace gaussian pyramid model methods. Using multi-scale image enhancement algorithm to enhance the High frequency component of the decomposed image, Then adjust the light of the low frequency part to make the reconstructed image illumination contrast more reasonable. The enhanced process according to different frequency layer image feature make the different gain weight for the different frequency layer image characteristics,so different frequency image layer realize respectively noise smoothing, dimensionality reduction and enhance the effect of edge character.The simulation experiments showed that this Image Processing Algorithm effect is very good.

Enhancement Algorithm of Color Fog Image Based on the Adaptive Scale and S-Cosine Curve

2014 ◽  
Vol 513-517 ◽  
pp. 3362-3367
Author(s):  
Yin Gao ◽  
Li Jun Yun ◽  
Jun Sheng Shi ◽  
Fei Yan Cheng
Keyword(s):  
Image Enhancement ◽  
Color Space ◽  
Weighted Average ◽  
Objective Evaluation ◽  
Gaussian Kernel ◽  
Correction Function ◽  
Multi Scale ◽  
Cosine Curve ◽  
Reflection Component ◽  
Enhancement Algorithm

To deal with the image contrast and color fidelity details problem in the traditional Center around the Retinex image enhancement algorithms, Enhancement algorithm of color fog image based on the adaptive scale and s-cosine curve is proposed. Firstly, the image is transformed into the RGB color space. Then the each channel pixel values can be stretched the grayscale range by S-cosine curve and introduces the local correction function. It can calculate the scale of the Gaussian kernel, and then proceeds to do the Gamma correction for the estimates of the reflection component, obtains the multi-scale image by the weighted average. Afterwards, the obtained image is used to global nonlinear correction, image sharpening and smoothing, and being superimposed reflection components, achieving the image enhancement. At last, it can carry on the intensity adjustment and grayscale adjustment for the obtained image. Through the subjective observation and objective evaluation, this algorithm is better than the traditional center around Retinex algorithm and MSRCR algorithm in processing effect.

scholarly journals Research on Image Enhancement Algorithm Based on Artificial Intelligence

2021 ◽  
Vol 2074 (1) ◽  
pp. 012024
Author(s):  
Jie Liu ◽  
Yuanyuan Peng
Keyword(s):  
Artificial Intelligence ◽  
Image Quality ◽  
Image Enhancement ◽  
Interpolation Method ◽  
Mapping Function ◽  
Exposure Fusion ◽  
Multi Scale ◽  
Fused Image ◽  
Artificial Intelligence Technology ◽  
Enhancement Algorithm

Abstract With the continuous development of social science and technology, people have higher and higher requirements for image quality. This paper integrates artificial intelligence technology and proposes a low-illuminance panoramic image enhancement algorithm based on simulated multi-exposure fusion. First, the image information content is used as a metric to estimate the optimal exposure rate, and the brightness mapping function is used to enhance the V component, and the low-illuminance. The image and the overexposed image are input, the medium exposure image is synthesized by the exposure interpolation method, and the low illumination image, the medium exposure image and the overexposure image are merged using a multi-scale fusion strategy to obtain the fused image, which is corrected by a multi-scale detail enhancement algorithm. After the fusion, the details are enhanced to obtain the final enhanced image. Practice has proved that the algorithm can effectively improve the image quality.

A New Method of Fundus Image Enhancement Based on Rough Set and Wavelet Transform

2013 ◽  
Vol 397-400 ◽  
pp. 2205-2208
Author(s):  
Wen Dong Zhao ◽  
You Dong Zhang ◽  
Chun Xia Jin
Keyword(s):  
Wavelet Transform ◽  
Image Enhancement ◽  
Rough Set ◽  
Fundus Images ◽  
Fundus Image ◽  
Visual Effects ◽  
Multi Scale ◽  
Fuzzy Enhancement ◽  
Enhancement Algorithm ◽  
Number Of Iterations

Fundus images are complex images with more details, and on the basis of the inadequate fuzzy enhancement algorithm proposed by Pal et al, this article propose an improved algorithm of rough set for fundus image enhancement. The fundus image will be multi-scale decomposed by wavelet transform firstly, and then the subgraphs are enhanced by using rough set to improve the visual effects; finally the processed sub-images will be reconstructed and generated a new-enhanced image. Compared with the Pal algorithm, the new algorithm not only overcomes its weaknesses that the threshold is set with a fixed value, but also reduces the number of iterations. Experimental results show that the improved enhancement algorithm has a better effect on the fundus image enhancement, and the various details of fundus images can be shown better.

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