A journal of IEEE and CAA , publishes high-quality papers in English on original theoretical/experimental research and development in all areas of automation
Advanced Search

IEEE/CAA Journal of Automatica Sinica

  • JCR Impact Factor: 18.3, Top 1 (SCI Q1)
    CiteScore: 28.2, Top 1% (Q1)
    Google Scholar h5-index: 95， TOP 5
Turn off MathJax
Article Contents
Article Navigation >  IEEE/CAA Journal of Automatica Sinica  > 2026  > In Press, Accepted Manuscript
H. Hong, S. Guo, L. Ye, L. Wang, B. Yang, Y. Zhu, and T. Zhang, “Pixel-by-pixel deblurring of an optical remote sensing satellite image with spatial-variant blur,” IEEE/CAA J. Autom. Sinica, early access, 2026. doi: 10.1109/JAS.2026.126026
Citation: H. Hong, S. Guo, L. Ye, L. Wang, B. Yang, Y. Zhu, and T. Zhang, “Pixel-by-pixel deblurring of an optical remote sensing satellite image with spatial-variant blur,” IEEE/CAA J. Autom. Sinica, early access, 2026. doi: 10.1109/JAS.2026.126026
shu

Pixel-by-Pixel Deblurring of An Optical Remote Sensing Satellite Image With Spatial-Variant Blur

doi: 10.1109/JAS.2026.126026
Funds:  This work was supported by National Natural Science Foundation of China (62550115)
More Information
    Abstract
  • When remote sensing satellites observe the Earth, the acquired images suffer from spatial-variant blurring degradation due to atmospheric turbulence and cross-medium factors. It means that the degree and causes of blurriness vary across different pixels. Traditional methods address such issues by applying a uniform blur kernel for restoration of the whole image or local blocks. They often fail to produce sufficiently clear results. To tackle this problem, this paper proposes a pixel-level deblurring method. Firstly, we combine gradient-domain structure computation with saliency detection algorithms. This allows us to extract multiple regions from the degraded image where valuable information is relatively concentrated. We calculate the point spread function (PSF) of these regions with a constraint by saliency values as blur kernels, and estimate the blur kernels at each pixel location based on spatial relationships. Finally, we solve for each pixel value in our proposed model rather than directly deconvolve with blur kernels, and refine the kernels and pixel values using iterative optimization. Experiments demonstrate that, as an unsupervised approach, our proposed method can achieve better performance and be more efficient than state-of-the-art methods, and even some deep-learning methods in real spatially variant blurred remote-sensing images.

     

    • FullText(HTML)
  • loading
    • References(53)
  • [1]
    E. Pan, Y. Ma, X. Mei, J. Huang, F. Fan, and J. Ma, “D2Net: Deep denoising network in frequency domain for hyperspectral image,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 3, pp. 813–815, Mar. 2023. doi: 10.1109/JAS.2022.106019
    [2]
    Q. Lian, W. Yan, X. Zhang, and S. Chen, “Single image rain removal using image decomposition and a dense network,” IEEE/CAA J. Autom. Sinica, vol. 6, no. 6, pp. 1428–1437, Nov. 2019. doi: 10.1109/jas.2019.1911441
    [3]
    H. Zhao, C. Xiao, J. Yu, and X. Xu, “Single image fog removal based on local extrema,” IEEE/CAA J. Autom. Sinica, vol. 2, no. 2, pp. 158–165, Apr. 2015.
    [4]
    X. Tong, Z. Ye, Y. Xu, S. Gao, H. Xie, Q. Du, S. Liu, X. Xu, S. Liu, K. Luan, and U. Stilla, “Image registration with Fourier-based image correlation: A comprehensive review of developments and applications,” IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., vol. 12, no. 10, pp. 4062–4081, Oct. 2019. doi: 10.1109/JSTARS.2019.2937690
    [5]
    F. Wen, R. Ying, Y. Liu, P. Liu, and T.-K. Truong, “A simple local minimal intensity prior and an improved algorithm for blind image deblurring,” IEEE Trans. Circuits Syst. Video Technol., vol. 31, no. 8, pp. 2923–2937, Aug. 2021. doi: 10.1109/TCSVT.2020.3034137
    [6]
    J. Han, S. L. Zhang, and Z. Ye, “Combined patch-wise minimal-maximal pixels regularization for deblurring,” ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., vol. V-1-2020, pp. 17–23, Aug. 2020. doi: 10.5194/isprs-annals-v-1-2020-17-2020
    [7]
    B. Rasti, Y. Chang, E. Dalsasso, L. Denis, and P. Ghamisi, “Image restoration for remote sensing: Overview and toolbox,” IEEE Geosci. Remote Sens. Mag., vol. 10, no. 2, pp. 201–230, Jun. 2022. doi: 10.1109/MGRS.2021.3121761
    [8]
    J. Li, X. Huang, L. Tu, T. Zhang, and L. Wang, “A review of building detection from very high resolution optical remote sensing images,” GIScience Remote Sens., vol. 59, no. 1, pp. 1199–1225, Aug. 2022. doi: 10.1080/15481603.2022.2101727
    [9]
    S. Peyghambari and Y. Zhang, “Hyperspectral remote sensing in lithological mapping, mineral exploration, and environmental geology: An updated review,” J. Appl. Remote Sens., vol. 15, no. 3, Art. no. 031501, Jul. 2021.
    [10]
    G. Camps-Valls, D. Tuia, L. Bruzzone, and J. A. Benediktsson, “Advances in hyperspectral image classification: Earth monitoring with statistical learning methods,” IEEE Signal Process. Mag., vol. 31, no. 1, pp. 45–54, Jan. 2014. doi: 10.1109/MSP.2013.2279179
    [11]
    G. Moser, S. B. Serpico, and J. A. Benediktsson, “Land-cover mapping by Markov modeling of spatial–contextual information in very-high-resolution remote sensing images,” Proc. IEEE, vol. 101, no. 3, pp. 631–651, Mar. 2013. doi: 10.1109/JPROC.2012.2211551
    [12]
    X. Zhi, S. Zhang, F. Yu, S. Jiang, J. Hu, and W. Chen, “Imaging characteristics and image restoration method for large field circular scanning system,” Results Phys., vol. 31, Art. no. 104884, Dec. 2021. doi: 10.1016/j.rinp.2021.104884
    [13]
    T. Zhang, H. Han-Yu, X. H. Sun, and Z. Song, “Restoring turbulence-degraded images based on estimation of turbulence point spread function values,” Acta Autom. Sinica, vol. 29, no. 4, pp. 573–581, 2003.
    [14]
    H. Shen, L. Du, L. Zhang, and W. Gong, “A blind restoration method for remote sensing images,” IEEE Geosci. Remote Sens. Lett., vol. 9, no. 6, pp. 1137–1141, Nov. 2012. doi: 10.1109/LGRS.2012.2190038
    [15]
    D. Llaveria, A. Camps, and H. Park, “Correcting the ADCS jitter induced blurring in small satellite imagery,” IEEE J. Miniat. Air Space Syst., vol. 1, no. 2, pp. 130–137, Sep. 2020. doi: 10.1109/JMASS.2020.3013440
    [16]
    Z. Zhang, L. Zheng, Y. Piao, S. Tao, W. Xu, T. Gao, and X. Wu, “Blind remote sensing image deblurring using local binary pattern prior,” Remote Sens., vol. 14, no. 5, Art. no. 1276, Mar. 2022. doi: 10.3390/rs14051276
    [17]
    H. Lim, S. Yu, K. Park, D. Seo, and J. Paik, “Texture-aware deblurring for remote sensing images using ℓ0-based deblurring and ℓ2-based fusion,” IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., vol. 13, pp. 3094–3108, Jun. 2020. doi: 10.1109/JSTARS.2020.2999961
    [18]
    J. Pan, Z. Hu, Z. Su, and M.-H. Yang, “Deblurring text images via L0-regularized intensity and gradient prior,” in Proc. IEEE Conf. Computer Vision and Pattern Recognition, Columbus, USA, 2014, pp. 2901−2908.
    [19]
    Q. Shan, J. Jia, and A. Agarwala, “High-quality motion deblurring from a single image,” in Proc. ACM SIGGRAPH Papers, Los Angeles, USA, 2008, Art. no. 73.
    [20]
    D. Krishnan, T. Tay, and R. Fergus, “Blind deconvolution using a normalized sparsity measure,” in Proc. CVPR, Colorado Springs, USA, 2011, pp. 233−240.
    [21]
    L. Xu and J. Jia, “Two-phase kernel estimation for robust motion deblurring,” in Proc. 11th European Conf. Computer Vision on Computer Vision - ECCV, Heraklion, Greece, 2010, pp. 157−170.
    [22]
    A. C. Likas and N. P. Galatsanos, “A variational approach for Bayesian blind image deconvolution,” IEEE Trans. Signal Process., vol. 52, no. 8, pp. 2222–2233, Aug. 2004. doi: 10.1109/TSP.2004.831119
    [23]
    M. Hirsch, S. Sra, B. Schölkopf, and S. Harmeling, “Efficient filter flow for space-variant multiframe blind deconvolution,” in Proc. IEEE Computer Society Conf. Computer Vision and Pattern Recognition, San Francisco, USA, 2010, pp. 607−614.
    [24]
    X. Yu, F. Xu, S. Zhang, and L. Zhang, “Efficient patch-wise non-uniform deblurring for a single image,” IEEE Trans. Multimedia, vol. 16, no. 6, pp. 1510–1524, Oct. 2014. doi: 10.1109/TMM.2014.2321734
    [25]
    A. Levin, “Blind motion deblurring using image statistics,” in Proc. 20th Int. Conf. Neural Information Processing Systems, Vancouver, Canada, 2007, pp. 841−848.
    [26]
    A. Gupta, N. Joshi, C. L. Zitnick, M. Cohen, and B. Curless, “Single image deblurring using motion density functions,” in Proc. 11th European Conf. Computer Vision on Computer Vision - ECCV, Heraklion, Greece, 2010, pp. 171−184.
    [27]
    X. Tao, H. Gao, X. Shen, J. Wang, and J. Jia, “Scale-recurrent network for deep image deblurring,” in Proc. IEEE/CVF Conf. Computer Vision and Pattern Recognition, Salt Lake City, USA, 2018, pp. 8174−8182.
    [28]
    O. Kupyn, T. Martyniuk, J. Wu, and Z. Wang, “DeblurGAN-v2: Deblurring (orders-of-magnitude) faster and better,” in Proc. IEEE/CVF Int. Conf. Computer Vision, Seoul, Korea (South), 2019, pp. 8877−8886.
    [29]
    Z. Chen, J. J. Gu, L. H. Kong, D. Liu, B. Xia, X. Yuan, and Y. L. Zhang, “Hierarchical integration diffusion model for realistic image deblurring,” in Proc. 37th Adv. Neural Inf. Process. Syst., New Orleans, USA, 2024.
    [30]
    M. Ren, M. Delbracio, H. Talebi, G. Gerig, and P. Milanfar, “Multiscale structure guided diffusion for image deblurring,” in Proc. IEEE/CVF Int. Conf. Computer Vision, Paris, France, 2023, pp. 10687−10699.
    [31]
    K. Zhang, W. Zuo, Y. Chen, D. Meng, and L. Zhang, “Beyond a Gaussian denoiser: Residual learning of deep CNN for image denoising,” IEEE Trans. Image Process., vol. 26, no. 7, pp. 3142–3155, Jul. 2017. doi: 10.1109/TIP.2017.2662206
    [32]
    Q. Zhao, H. Wang, Z. Yue, and D. Meng, “A deep variational Bayesian framework for blind image deblurring,” Knowl.-Based Syst., vol. 249, Art. no. 109008, Aug. 2022. doi: 10.1016/j.knosys.2022.109008
    [33]
    A. Levin, Y. Weiss, F. Durand, and W. T. Freeman, “Understanding and evaluating blind deconvolution algorithms,” in Proc. IEEE Conf. Computer Vision and Pattern Recognition, Miami, USA, 2009, pp. 1964−1971.
    [34]
    D. Krishnan and R. Fergus, “Fast image deconvolution using hyper-laplacian priors,” in Proc. 23rd Int. Conf. Neural Information Processing Systems, Vancouver, Canada, 2009, pp. 1033−1041.
    [35]
    J. Zhang, D. Zhao, R. Xiong, S. Ma, and W. Gao, “Image restoration using joint statistical modeling in a space-transform domain,” IEEE Trans. Circuits Syst. Video Technol., vol. 24, no. 6, pp. 915–928, Jun. 2014. doi: 10.1109/TCSVT.2014.2302380
    [36]
    Z. Song, Z. Zhang, F. Fang, Z. Fan, and J. Lu, “Deep semantic-aware remote sensing image deblurring,” Signal Process., vol. 211, Art. no. 109108, Oct. 2023. doi: 10.1016/j.sigpro.2023.109108
    [37]
    R. Achanta, S. Hemami, F. Estrada, and S. Susstrunk, “Frequency-tuned salient region detection,” in Proc. IEEE Conf. Computer Vision and Pattern Recognition, Miami, USA, 2009, pp. 1597−1604.
    [38]
    L. Xu, S. Zheng, and J. Jia, “Unnatural L0 sparse representation for natural image deblurring,” in Proc. IEEE Conf. Computer Vision and Pattern Recognition, Portland, USA, 2013, pp. 1107−1114.
    [39]
    S. Osher and L. I. Rudin, “Feature-oriented image enhancement using shock filters,” SIAM J. Numer. Anal., vol. 27, no. 4, pp. 919–940, Aug. 1990. doi: 10.1137/0727053
    [40]
    S. Cao, H. Fang, L. Chen, W. Zhang, Y. Chang, and L. Yan, “Robust blind deblurring under stripe noise for remote sensing images,” IEEE Trans. Geosci. Remote Sens., vol. 60, Art. no. 5412117, Oct. 2022. doi: 10.1109/tgrs.2022.3202867
    [41]
    L. Li, D. Wu, J. Wu, H. Li, W. Lin, and A. C. Kot, “Image sharpness assessment by sparse representation,” IEEE Trans. Multimedia, vol. 18, no. 6, pp. 1085–1097, Jun. 2016. doi: 10.1109/TMM.2016.2545398
    [42]
    O. Whyte, J. Sivic, A. Zisserman, and J. Ponce, “Non-uniform deblurring for shaken images,” Int. J. Comput. Vis., vol. 98, no. 2, pp. 168–186, Jun. 2012. doi: 10.1007/s11263-011-0502-7
    [43]
    G. Cheng, J. Han, and X. Lu, “Remote sensing image scene classification: Benchmark and state of the art,” Proc. IEEE, vol. 105, no. 10, pp. 1865–1883, Oct. 2017. doi: 10.1109/JPROC.2017.2675998
    [44]
    B. Zhao, Y. Zhong, G. S. Xia, and L. Zhang, “Dirichlet-derived multiple topic scene classification model for high spatial resolution remote sensing imagery,” IEEE Trans. Geosci. Remote Sens., vol. 54, no. 4, pp. 2108–2123, Apr. 2016. doi: 10.1109/TGRS.2015.2496185
    [45]
    J. Pan, Z. Hu, Z. Su, and M.-H. Yang, “ℓ0-regularized intensity and gradient prior for deblurring text images and beyond,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 39, no. 2, pp. 342–355, Feb. 2017. doi: 10.1109/TPAMI.2016.2551244
    [46]
    L. Kong, J. Dong, J. Ge, M. Li, and J. Pan, “Efficient frequency domain-based transformers for high-quality image deblurring,” in Proc. IEEE/CVF Conf. Computer Vision and Pattern Recognition, Vancouver, Canada, 2023, pp. 5886−5895.
    [47]
    X. Hua, Z. Li, and H. Hong, “An efficient multiscale spatial rearrangement MLP architecture for image restoration,” IEEE Trans. Image Process., vol. 33, pp. 423–438, Jan. 2024. doi: 10.1109/TIP.2023.3341700
    [48]
    Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, “Image quality assessment: from error visibility to structural similarity,” IEEE Trans. Image Process., vol. 13, no. 4, pp. 600–612, Apr. 2004. doi: 10.1109/TIP.2003.819861
    [49]
    H. Hong, Z. Zuo, Y. Shi, X. Hua, L. Xiong, Y. Zhang, and T. Zhang, “Adaptive anisotropic pixel-by-pixel correction method for a space-variant degraded image,” J. Opt. Soc. Am. A, vol. 40, no. 9, pp. 1686–1697, Aug. 2023. doi: 10.1364/JOSAA.490150
    [50]
    A. E. Savakis and H. J. Trussell, “Blur identification by residual spectral matching,” IEEE Trans. Image Process., vol. 2, no. 2, pp. 141–151, Apr. 1993. doi: 10.1109/83.217219
    [51]
    D. G. Sheppard, B. R. Hunt, and M. W. Marcellin, “Iterative multiframe superresolution algorithms for atmospheric-turbulence-degraded imagery,” J. Opt. Soc. Am. A, vol. 15, no. 4, pp. 978–992, 1998. doi: 10.1364/JOSAA.15.000978
    [52]
    D. Perrone and P. Favaro, “Total variation blind deconvolution: The devil is in the details,” in Proc. IEEE Conf. Computer Vision and Pattern Recognition, Columbus, USA, 2014, pp. 2909−2916.
    [53]
    Z. Wang, X. Zeng, Z. Yan, J. Kang, and X. Sun, “Air-PolSAR-Seg: A large-scale data set for terrain segmentation in complex-scene PolSAR images,” IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., vol. 15, pp. 3830–3841, Jan. 2022. doi: 10.1109/JSTARS.2022.3170326
    • Related Articles
    • Supplements(0)

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Figures(12)  / Tables(4)

    Get Citation
    PDF
    XML

    Article Metrics

    Article views (50) PDF downloads(21) Cited by()

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return