Capsule Endoscopy Image Deblurring Method Based on Multi-scale Recurrent Network

During the process of acquiring capsule endoscope images, image motion blur may result from errors made by the operating physician. A multi-scale recurrent attention network is proposed to address the issue of motion blur in collected images. Considering the issue of over-exposure during image acqui...

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Bibliographic Details
Published in:Journal of physics. Conference series 2023-11, Vol.2640 (1), p.12003
Main Authors: Yang, Shiyu, Guo, Tai, Zhan, Ruoya
Format: Article
Language:English
Subjects:
Blurring
Endoscopes
Endoscopy
Image acquisition
Image enhancement
Modules
Physics
Signal to noise ratio
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Summary:During the process of acquiring capsule endoscope images, image motion blur may result from errors made by the operating physician. A multi-scale recurrent attention network is proposed to address the issue of motion blur in collected images. Considering the issue of over-exposure during image acquisition in capsule endoscopy, which can adversely affect network generation results, an image preprocessing module has been incorporated into the network to eliminate highlights caused by over-exposure in input images. To enhance network sampling efficiency, the encoder is equipped with a Convolutional Block Attention Module (CBAM) to extract fuzzy features more effectively. Additionally, depthwise separable convolution is employed to reduce parameter count. Since the structure of images obtained through capsule endoscopy is relatively simple and stable, we have opted for the mean square error as our loss function due to its heightened sensitivity to data errors. The experimental results demonstrate that the proposed network achieves a Peak Signal-to-Noise Ratio (PSNR) of 31.096 and a Structural Similarity Index Measure (SSIM) of 0.925, indicating a significant improvement over DeblurGAN-v2 and Pix2pix.
ISSN:1742-6588
1742-6596
DOI:10.1088/1742-6596/2640/1/012003