Adaptive image enhancement algorithm based on fuzzy entropy and human visual characteristics

2018 ◽  
Vol 29 (5) ◽  
pp. 1079
Keyword(s):  
Image Enhancement ◽  
Fuzzy Entropy ◽  
Visual Characteristics ◽  
Enhancement Algorithm ◽  
Human Visual Characteristics

A New Medical Image Enhancement Method Based on Human Visual Characteristics

2014 ◽  
Vol 543-547 ◽  
pp. 2543-2546
Author(s):  
Ai Bin Dong ◽  
Yun Feng Zhang ◽  
Yi Fang Liu
Keyword(s):  
Visual System ◽  
Image Enhancement ◽  
Human Visual System ◽  
Medical Image ◽  
Region Of Interest ◽  
Enhancement Method ◽  
Visual Characteristics ◽  
Medical Image Enhancement ◽  
Human Visual Characteristics

Studying of image enhancement shows that the quality of image heavily relies on human visual system. In this paper, we apply this fact to design a new image enhancement method for medical images that improves the detail regions. First, the eye region of interest (ROI) is segmented; then the Un-sharp Masking (USM) is used to enhance the detail regions. Experiments show that the proposed method can effectively improve the accuracy of medical image enhancement and has a significant effect.

CONTRAST ENHANCEMENT USING TEXTURE HISTOGRAM AND FUZZY ENTROPY

2007 ◽  
Vol 03 (03) ◽  
pp. 349-365
Author(s):  
YANHUI GUO ◽  
H. D. CHENG ◽  
JIANHUA HUANG ◽  
WEI ZHAO ◽  
XIANGLONG TANG
Keyword(s):  
Contrast Enhancement ◽  
Image Enhancement ◽  
Fuzzy Entropy ◽  
Experimental Results ◽  
Noise Amplification ◽  
Enhancement Algorithm

Image enhancement is used to correct contrast deficiencies and to improve the quality of an image. It is essential and critical to extracting features and segmenting images. This paper presents a novel contrast enhancement algorithm based on newly defined texture histogram and fuzzy entropy with the ability to preserve edges and details, while avoiding noise amplification and over-enhancement. To demonstrate the performance, the proposed algorithm is tested on a variety of images and compared with other enhancement algorithms. Experimental results proved that the proposed method has better performance in enhancing images without over-enhancement and under-enhancement.

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