Skip to main content

Deep unsupervised endoscopic image enhancement based on multi-image fusion.

Huang, D., Liu, J., Zhou, S. and Tang, W., 2022. Deep unsupervised endoscopic image enhancement based on multi-image fusion. Computer Methods and Programs in Biomedicine, 221 (June), 106800.

Full text available as:

Manuscript_2020_5_22__1_.pdf - Accepted Version
Available under License Creative Commons Attribution Non-commercial.


DOI: 10.1016/j.cmpb.2022.106800


Background and objective: A deep unsupervised endoscopic image enhancement method is proposed based on multi-image fusion to achieve high quality endoscope images from poorly illuminated, low contrast and color deviated images through an unsupervised mapping and deep learning network without the need for ground truth. Methods: Firstly, three image enhancement methods are used to process original endoscopic images to obtain three derived images, which are then transformed into HSI color space. Secondly, a deep unsupervised multi-image fusion network (DerivedFuse) is proposed to extract and fuse features of the derived images accurately by utilizing a new no-reference quality metric as loss function. I-channel images of the three derived images are inputted into the DerivedFuse network to enhance the intensity component of the original image. Finally, a saturation adjustment function is proposed to adaptive adjusting the saturation component of HSI color space to enrich the color information of the original input image. Results: Three evaluation metrics: Entropy, Contrast Improvement Index (CII) and Average Gradient (AG) are used to evaluate the performance of the proposed method. The results are compared with that of fourteen state-of-the-art algorithms. Experiments on endoscopic image enhancement show that the Entropy value of our method is 3.27% higher than the optimal entropy value of comparison algorithms. The CII of our proposed method is 6.19% higher than that of comparison algorithms. The AG of our method is 7.83% higher than the optimal AG of comparison algorithms. Conclusions: The proposed deep unsupervised multi-image fusion method can obtain image information details, enhance endoscopic images with high contrast, rich and natural color information, visual and image quality. Sixteen doctors and medical students have given their assessments on the proposed method for assisting clinical diagnoses.

Item Type:Article
Uncontrolled Keywords:Derived image; Endoscopic image enhancement; HSI color space; Image fusion; Unsupervised deep learning; Algorithms; Color; Humans; Image Enhancement; Image Processing, Computer-Assisted
Group:Faculty of Science & Technology
ID Code:37149
Deposited By: Symplectic RT2
Deposited On:05 Jul 2022 13:28
Last Modified:06 May 2023 01:08


Downloads per month over past year

More statistics for this item...
Repository Staff Only -