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Keywords = secondary weighted guided filtering

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17 pages, 4168 KB  
Article
A Medical Endoscope Image Enhancement Method Based on Improved Weighted Guided Filtering
by Guo Zhang, Jinzhao Lin, Enling Cao, Yu Pang and Weiwei Sun
Mathematics 2022, 10(9), 1423; https://doi.org/10.3390/math10091423 - 23 Apr 2022
Cited by 17 | Viewed by 3842
Abstract
In clinical surgery, the quality of endoscopic images is degraded by noise. Blood, illumination changes, specular reflection, smoke, and other factors contribute to noise, which reduces the quality of an image in an occluded area, affects doctors’ judgment, prolongs the operation duration, and [...] Read more.
In clinical surgery, the quality of endoscopic images is degraded by noise. Blood, illumination changes, specular reflection, smoke, and other factors contribute to noise, which reduces the quality of an image in an occluded area, affects doctors’ judgment, prolongs the operation duration, and increases the operation risk. In this study, we proposed an improved weighted guided filtering algorithm to enhance endoscopic image tissue. An unsharp mask algorithm and an improved weighted guided filter were used to enhance vessel details and contours in endoscopic images. The scheme of the entire endoscopic image processing, which included detail enhancement, contrast enhancement, brightness enhancement, and highlight area removal, is presented. Compared with other algorithms, the proposed algorithm maintained edges and reduced halos efficiently, and its effectiveness was demonstrated using experiments. The peak signal-to-noise ratio and structural similarity of endoscopic images obtained using the proposed algorithm were the highest. The foreground–background detail variance–background variance improved. The proposed algorithm had a strong ability to suppress noise and could maintain the structure of original endoscopic images, which improved the details of tissue blood vessels. The findings of this study can provide guidelines for developing endoscopy devices. Full article
(This article belongs to the Special Issue Computer Vision and Pattern Recognition with Applications)
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