Technology of Graphic & Image
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App. Res. of Comp. Printed Article
Vol. 41, 2024 No. 1
282-287

Restoration method for atmospheric turbulence degraded images based on generative inversion

Cui Haoran
Miao Zhuang
Wang Jiabao
Yu Peiyi
Wang Peilong
College of Command & Control Engineering, Army Engineering University of PLA, Nanjing 210007, China
DOI: 10.19734/j.issn.1001-3695.2023.07.0267

Abstract

Atmospheric turbulence is a crucial factor that affects the quality of long-distance imaging. Though current deep learning models can effectively suppress geometric displacement and spatial blurring caused by atmospheric turbulence, such models require a large number of parameters and computational resources. To tackle this problem, this paper proposed a lightweight atmospheric turbulence degraded image restoration model based on generative inversion that entailed three core mo-dules: the DeBlur module, the remove shift module, and the turbulence regeneration module. The DeBlur module used high-dimensional feature mapping blocks, detail feature extraction blocks, and feature compensation blocks to suppress image blurring caused by turbulence. The remove shift module compensated for pixel displacement caused by turbulence using two convolutional layers. The turbulence regeneration module regenerated turbulence degraded images through convolutional operations. In the DeBlur module, it designed an attention-based feature compensation module that integrated the channel attention mechanism and the spatial mixed attention mechanism to focus on essential detail information in the image during training. The proposed model achieved peak signal-to-noise ratios of 19.94 dB and 23.51 dB, and structural similarity values of 0.688 2 and 0.752 1 on publicly available dataset Heat Chamber and self-built dataset Helen, respectively. Furthermore, it reduced the number of parameters and computational resources, compared to the current state-of-the-art(SOTA) method. The experimental results demonstrate the effectiveness of this method in restoring atmospheric turbulence degraded images.

Foundation Support

江苏省自然科学基金资助项目

Publish Information

DOI: 10.19734/j.issn.1001-3695.2023.07.0267
Publish at: Application Research of Computers Printed Article, Vol. 41, 2024 No. 1
Section: Technology of Graphic & Image
Pages: 282-287
Serial Number: 1001-3695(2024)01-045-0282-06

Publish History

[2023-08-22] Accepted Paper
[2024-01-05] Printed Article

Cite This Article

崔浩然, 苗壮, 王家宝, 等. 基于生成逆推的大气湍流退化图像复原方法 [J]. 计算机应用研究, 2024, 41 (1): 282-287. (Cui Haoran, Miao Zhuang, Wang Jiabao, et al. Restoration method for atmospheric turbulence degraded images based on generative inversion [J]. Application Research of Computers, 2024, 41 (1): 282-287. )

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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