Adaptive Wavelet Thresholding Algorithm on Low-Contrast Image

2009 ◽  
pp. 289-294
Author(s):  
Bo Yang ◽  
Xue Wang ◽  
Liang Lei
Keyword(s):  
Wavelet Thresholding ◽  
Adaptive Wavelet ◽  
Low Contrast ◽  
Thresholding Algorithm ◽  
Contrast Image

Application of Adaptive Wavelet Thresholding to Ultrasonic Signal Compression of Aluminum Alloy Forgings

2013 ◽  
Vol 658 ◽  
pp. 89-92
Author(s):  
Shou Shan Liu ◽  
Chuan Jiang Wang ◽  
Li Jun Bi ◽  
Chang Zhi Lv
Keyword(s):  
Aluminum Alloy ◽  
Ultrasonic Signal ◽  
Wavelet Thresholding ◽  
Signal Compression ◽  
Hard Thresholding ◽  
Coherent Noise ◽  
Adaptive Wavelet ◽  
Effective Signal ◽  
Thresholding Algorithm ◽  
The Mathematical Model

In this paper, for the purpose of ultrasonic signal compression and the coherent noise depressing in nondestructive test of aluminum alloy forging, the mathematical model of defect echoes is discussed and confirmed. And then the wavelet kernel is also confirmed according the waveform of the defect echoes. As the algorithms of standard hard thresholding and soft thresholding of wavelet transform can not bring out effective compression and depression to the coherent noise, an adaptive wavelet thresholding algorithm is introduced. Experimental results indicate that the adaptive wavelet thresholding algorithm can offer effective signal compression and depression to the coherent noise.

Gamma corrected reflectance for low contrast image enhancement using guided filter

2022 ◽  
Author(s):  
Ashish Kumar Bhandari ◽  
Kankanala Srinivas ◽  
Shubham Maurya
Keyword(s):  
Image Enhancement ◽  
Guided Filter ◽  
Low Contrast ◽  
Contrast Image

Research on low contrast image enhancement technology

2021 ◽  
Author(s):  
Jiale Yao ◽  
Xiangsuo Fan ◽  
Yixun Chen ◽  
Wuchao Li
Keyword(s):  
Image Enhancement ◽  
Low Contrast ◽  
Enhancement Technology ◽  
Contrast Image

How Good is Too Good? A Subjective Study on Over Enhancement of Images

2021 ◽  
Vol 2021 (29) ◽  
pp. 83-88
Author(s):  
Sahar Azimian ◽  
Farah Torkamani Azar ◽  
Seyed Ali Amirshahi
Keyword(s):  
Image Quality ◽  
High Contrast ◽  
Current Image ◽  
Low Contrast ◽  
Image Dataset ◽  
Long Time ◽  
Reference Images ◽  
Subjective Study ◽  
Contrast Image

For a long time different studies have focused on introducing new image enhancement techniques. While these techniques show a good performance and are able to increase the quality of images, little attention has been paid to how and when overenhancement occurs in the image. This could possibly be linked to the fact that current image quality metrics are not able to accurately evaluate the quality of enhanced images. In this study we introduce the Subjective Enhanced Image Dataset (SEID) in which 15 observers are asked to enhance the quality of 30 reference images which are shown to them once at a low and another time at a high contrast. Observers were instructed to enhance the quality of the images to the point that any more enhancement will result in a drop in the image quality. Results show that there is an agreement between observers on when over-enhancement occurs and this point is closely similar no matter if the high contrast or the low contrast image is enhanced.

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