An Adaptive Fuzzy Image Enhancement Algorithm for Local Regions

2006 ◽  
Author(s):  
Yan Maode ◽  
Bo Shaobo ◽  
Li Xue ◽  
He Yuyao
Keyword(s):  
Image Enhancement ◽  
Adaptive Fuzzy ◽  
Fuzzy Image ◽  
Enhancement Algorithm

scholarly journals Design and implementation of fuzzy image enhancement system in police application

2018 ◽  
Vol 232 ◽  
pp. 02041
Author(s):  
Cheng Liu ◽  
Fan-liang Bu
Keyword(s):  
Image Enhancement ◽  
Processing System ◽  
Processing Efficiency ◽  
C Language ◽  
Evaluation Indexes ◽  
Fuzzy Image ◽  
Material Evidence ◽  
Investigation Process ◽  
Enhancement Algorithm ◽  
Selection Of

A set of image processing system based on fuzzy image enhancement algorithm is designed and implemented in the light of the actual needs of image material evidence extraction in the criminal investigation process of public security department, which is combined with the computer technology such as C++ language and graphic development tools. The system can enhance the processing efficiency by continuously selecting different algorithms for input images. The same type of fuzzy image can be processed by various algorithms and the ideal image can be obtained by comparing the image evaluation indexes, and the optimal selection of image quality and algorithm is realized.

scholarly journals A Low-Light Sensor Image Enhancement Algorithm Based on HSI Color Model

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3583 ◽  
Author(s):  
Shiping Ma ◽  
Hongqiang Ma ◽  
Yuelei Xu ◽  
Shuai Li ◽  
Chao Lv ◽  
...  
Keyword(s):  
Image Enhancement ◽  
Color Space ◽  
Color Model ◽  
Low Light ◽  
Image Brightness ◽  
Low Contrast ◽  
Light Sensor ◽  
Hsi Color Model ◽  
Art Research ◽  
Enhancement Algorithm

Images captured by sensors in unpleasant environment like low illumination condition are usually degraded, which means low visibility, low brightness, and low contrast. In order to improve this kind of images, in this paper, a low-light sensor image enhancement algorithm based on HSI color model is proposed. At first, we propose a dataset generation method based on the Retinex model to overcome the shortage of sample data. Then, the original low-light image is transformed from RGB to HSI color space. The segmentation exponential method is used to process the saturation (S) and the specially designed Deep Convolutional Neural Network is applied to enhance the intensity component (I). At the end, we back into the original RGB space to get the final improved image. Experimental results show that the proposed algorithm not only enhances the image brightness and contrast significantly, but also avoids color distortion and over-enhancement in comparison with some other state-of-the-art research papers. So, it effectively improves the quality of sensor images.

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