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Volume 11 Issue 11
Nov.  2024

IEEE/CAA Journal of Automatica Sinica

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Article Navigation >  IEEE/CAA Journal of Automatica Sinica  > 2024  >  11(11): 2253-2269
K. Jiang, R. Wang, Y. Xiao, J. Jiang, X. Xu, and  T. Lu,  “Image enhancement via associated perturbation removal and texture reconstruction learning,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 11, pp. 2253–2269, Nov. 2024. doi: 10.1109/JAS.2024.124521
Citation: K. Jiang, R. Wang, Y. Xiao, J. Jiang, X. Xu, and  T. Lu,  “Image enhancement via associated perturbation removal and texture reconstruction learning,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 11, pp. 2253–2269, Nov. 2024. doi: 10.1109/JAS.2024.124521
shu

Image Enhancement via Associated Perturbation Removal and Texture Reconstruction Learning

doi: 10.1109/JAS.2024.124521
Funds:  This work was supported by the National Natural Science Foundation of China (U23B2009, 62376201, 423B2104) and Open Foundation (ZNXX2023MSO2, HBIR202311)
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    Abstract

  • Degradation under challenging conditions such as rain, haze, and low light not only diminishes content visibility, but also results in additional degradation side effects, including detail occlusion and color distortion. However, current technologies have barely explored the correlation between perturbation removal and background restoration, consequently struggling to generate high-naturalness content in challenging scenarios. In this paper, we rethink the image enhancement task from the perspective of joint optimization: Perturbation removal and texture reconstruction. To this end, we advise an efficient yet effective image enhancement model, termed the perturbation-guided texture reconstruction network (PerTeRNet). It contains two sub-networks designed for the perturbation elimination and texture reconstruction tasks, respectively. To facilitate texture recovery, we develop a novel perturbation-guided texture enhancement module (PerTEM) to connect these two tasks, where informative background features are extracted from the input with the guidance of predicted perturbation priors. To alleviate the learning burden and computational cost, we suggest performing perturbation removal in a sub-space and exploiting super-resolution to infer high-frequency background details. Our PerTeRNet has demonstrated significant superiority over typical methods in both quantitative and qualitative measures, as evidenced by extensive experimental results on popular image enhancement and joint detection tasks. The source code is available at

    https://github.com/kuijiang94/PerTeRNet

    .

     

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    Highlights

    • This study investigates the image enhancement tasks from a fresh perspective that involves the joint representation of perturbation removal, texture reconstruction and their association
    • It develops a perturbation-guided texture enhancement module (PerTEM) to associate degradation simulation and texture restoration, facilitating the learning capability while maintaining the model compactness
    • Experiments on various mainstream image enhancement tasks, such as image deraining, image dehazing and low-light image enhancement have demonstrated that PerTeRNet delivers competitive performance compared to the state-of-the-art method

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