Technology of Graphic & Image
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App. Res. of Comp. Printed Article
Vol. 40, 2023 No. 4
1233-1238

Sampling slice convolution for cross-scale feature fusion and endoscopic image deblurring

Yan Jingyi1,2
Li Xiaoxia1,2
Qin Jiamin3
Wen Liming3
Zhou Yingyue1,2
1. School of Information Engineering, Southwest University of Science & Technology, Mianyang Sichuan 621010, China
2. Robot Technology Used for Special Environment Key Laboratory of Sichuan Province, Mianyang Sichuan 621010, China
3. Sichuan Mianyang 404 Hospital, Mianyang Sichuan 621010, China
DOI: 10.19734/j.issn.1001-3695.2022.07.0392

Abstract

To solve the challenges of semantic information extraction and texture reconstruction in the process of endoscopic image deblurring, this paper designed a new sampling slice convolution(SSC) and applied it to the cross-scale feature fusion process. It divided the large-scale features into small-scale feature blocks losslessly by sampling at equal intervals, and then fused these feature blocks with small-scale features through convolution. All values of large-scale features participate in the feature fusion process, which could avoid the loss of detailed information. There was no interpolation operation on small-scale features, which could avoid the blurring of their semantic information. This paper proposed a feature interaction fusion(FIF) module, which used semantic features to activate detailed features, and then fused the two to achieve feature complementarity. This paper designed gradient reconstruction and frequency domain reconstruction loss functions for the feature differences of the bright channel, middle channel, and dark channel of endoscopic images to improve the sharpness of reconstructed images. Experiments on EAD and Kvasir-SEG datasets show that the PSNR of the algorithm reaches 32.88 dB and 33.01 dB, respectively, and the SSIM reaches 0.972 and 0.973, respectively. The experimental results show that the performance of the proposed algorithm is better than that of the mainstream deblurring algorithms, and the texture of the reconstructed image is visually clearer and does not produce artifacts.

Foundation Support

国家自然科学基金资助项目(62071399)
四川省科技计划重点研发项目(2021YFG0383)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2022.07.0392
Publish at: Application Research of Computers Printed Article, Vol. 40, 2023 No. 4
Section: Technology of Graphic & Image
Pages: 1233-1238
Serial Number: 1001-3695(2023)04-044-1233-06

Publish History

[2022-10-19] Accepted Paper
[2023-04-05] Printed Article

Cite This Article

严靖易, 李小霞, 秦佳敏, 等. 抽样切分卷积实现跨尺度特征融合及内镜图像去模糊 [J]. 计算机应用研究, 2023, 40 (4): 1233-1238. (Yan Jingyi, Li Xiaoxia, Qin Jiamin, et al. Sampling slice convolution for cross-scale feature fusion and endoscopic image deblurring [J]. Application Research of Computers, 2023, 40 (4): 1233-1238. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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