سابِق|تال

Super resolution reconstruction of MODIS image based on topic learning and sparse representation

  • role： First author第一作者
  • Affiliation：

    Faculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo 315211, China

  • Email：nbu.zhouf@gmail.com
  • Introduction：周峰(1990—　)，男，硕士研究生，研究方向为数字图像处理、图像超分辨率重建理论和应用。E-mail：nbu.zhouf@gmail.com
ZHOU Feng,  
  • role： Corresponding author通信作者
  • Affiliation：

    Faculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo 315211, China

  • Email：xyjw1969@126.com
  • Introduction：金炜(1969—　)，男，副教授，研究方向为压缩感知、模式识别等。E-mail：xyjw1969@126.com
JIN Wei*,  
  • Affiliation：

    Faculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo 315211, China

  • Email：xyjw1969@126.com
GONG Fei,  
  • Affiliation：

    Faculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo 315211, China

FU Randi

ملخص

MODIS images have important application value in the field of ground monitoring, cloud classification, and meteorological research. However, their image resolutions are still limited to a certain level because of the sensor limitations and external disturbance. This study attempts to reconstruct high-resolution MODIS images that make the edge clearer and more detailed by utilizing topic learning and the sparse representation method. The application value of existing MODIS images is then improved. A super resolution reconstruction method for MODIS images based on topic learning and sparse representation is proposed. The smoothing and texture parts of MODIS images are separated by the bilateral filtering method. The texture part is regarded as a training sample composed of several “documents”. The latent semantic features of the “document” are extracted by probabilistic Latent Semantic Analysis (pLSA) to discover the inherent “topics” of “document”. The improved K-SVD method trains several high- and low-resolution dictionary pairs that are suitable to different topics based on the aforementioned scenario, where the image blocks correspond to each topic. The probabilistic latent semantic analysis method is utilized in the reconstruction phase to adaptively select the image block topic, combine the dictionary of the corresponding topic, and reconstruct the high-resolution MODIS image through the sparse coding method. First, the MODIS image is blurred and subjected to down sampling processing in the experiment process to obtain a low-resolution image. Super resolution reconstruction is performed by utilizing different methods. The PSNR and SSIM of the original high-resolution and reconstructed images were compared utilizing different methods. Results show that the PSNR of the reconstructed image by our method is higher by approximately 1 dB and 0.5 dB than the bicubic interpolation and SCSR method, respectively. Its SSIM value is also higher than those of the other methods. The visual effects of super resolution reconstruction on the real images by different methods were compared. The experimental results show that the reconstructed images by our method have a high contrast ratio and rich texture details. The human vision is more sensitive to the image texture. This study separates the smoothing and texture parts of the MODIS image through the bilateral filter. The texture part is divided into multiple topics by probabilistic latent semantic analysis. A local adaptive super resolution method is constructed, which overcomes the problem of the adaptive selection of a reasonable dictionary according to the local characteristics of MODIS images. This process was conducted under the topic model framework combined with the improved K-SVD dictionary training methods, which train several high- and low-resolution dictionary pairs suitable to different topics. The experimental results show that the multi-dictionary reconstruction method can be utilized to represent MODIS images more sparsely and enhance the image reconstruction details. The experimental results also show that the reconstructed image is superior to the traditional method in terms of the visual effects, PSNR, and SSIM.

مفهوم

topic learning;probabilistic latent semantic analysis;sparse representation;super resolution;MODIS image

References

  1. 1.
    Aharon M, Elad M and Bruckstein A. 2006. rmK-SVD: an algorithm for designing overcomplete dictionaries for sparse representation. IEEE Transactions on Signal Processing, 54(11): 4311–4322
  2. 2.
    Aumann H H, Chahine M T, Gautier C, Goldberg M D, Kalnay E, McMillin L M, Revercomb H, Rosenkranz P W, Smith W L, Staelin D H, Strow L L and Susskind J. 2003. AIRS/AMSU/HSB on the Aqua mission: design, science objectives, data products, and processing systems. IEEE Transactions on Geoscience and Remote Sensing, 41(2): 253–264
  3. 3.
    Dong W S, Zhang L, Shi G M and Wu X L. 2011. Image deblurring and super-resolution by adaptive sparse domain selection and adaptive regularization. IEEE Transactions on Image Processing, 20(7): 1838–1857
  4. 4.
    Harris J L. 1964. Diffraction and resolving power. Journal of the Optical Society of America, 54(7): 931–936
  5. 5.
    Hofmann T. 1999a. Probabilistic latent semantic analysis // Proceedings of the 15th Conference on Uncertainty in Artificial Intelligence. Stockholm, Sweden: Morgan Kaufmann Publishers Inc.: 289–296
  6. 6.
    Hofmann T. 1999b. Probabilistic latent semantic indexing // Proceedings of the 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. Berkeley, California, USA: ACM: 50–57 [DOI: 10.1145/312624.312649]
  7. 7.
    Irani M and Peleg S. 1991. Improving resolution by image registration. CVGIP: Graphical Models and Image Processing, 53(3): 231–239
  8. 8.
    Jain A K. 1989. Fundamentals of Digital Image Processing. Englewood Cliffs, NJ: Prentice Hall
  9. 9.
    Li J, Yuan Q Q, Shen H F, Meng X C and Zhang L P. 2016. Hyperspectral image super-resolution by spectral mixture analysis and spatial–spectral group sparsity. IEEE Geoscience and Remote Sensing Letters, 13(9): 1250–1254
  10. 10.
    Liu Z, Yang J and Chen L. 2015. Super-resolution image restoration based on nonlocal sparse coding. Journal of Electronics and Information Technology, 37(3): 522–528
  11. 11.
    Pan Z X, Yu J, Xiao C B and Sun W D. 2015. Single image super resolution based on adaptive multi-dictionary learning. Acta Electronica Sinica, 43(2): 209–216
  12. 12.
    Parker J A, Kenyon R V and Troxel D E. 1983. Comparison of interpolating methods for image resampling. IEEE Transactions on Medical Imaging, 2(1): 31–39
  13. 13.
    Purkait P and Chanda B. 2013. Image upscaling using multiple dictionaries of natural image patches // Proceedings of the 11th Asian Conference on Computer Vision. Berlin Heidelberg: Springer: 284–295 [DOI: 10.1007/978-3-642-37431-9_22]
  14. 14.
    Schultz R R and Stevenson R L. 1994. A Bayesian approach to image expansion for improved definition. IEEE Transactions on Image Processing, 3(3): 233–242
  15. 15.
    Shen H F, Li P X and Zhang L P. 2006. Adaptive regularized MAP super-resolution reconstruction method. Geomatics and Information Science of Wuhan University, 31(11): 949–952
  16. 16.
    Shen H F, Peng L, Yue L W, Yuan Q Q and Zhang L P. 2016. Adaptive norm selection for regularized image restoration and super-resolution. IEEE transactions on cybernetics, 46(6): 1388–1399
  17. 17.
    Smith L N and Elad M. 2013. Improving dictionary learning: multiple dictionary updates and coefficient reuse. IEEE Signal Processing Letters, 20(1): 79–82
  18. 18.
    Yang J C, Wright J, Huang T and Ma Y. 2008. Image super-resolution as sparse representation of raw image patches // Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Anchorage, AK: IEEE: 1–8 [DOI: 10.1109/CVPR.2008.4587647]
  19. 19.
    Zeyde R, Elad M and Protter M. 2010. On single image scale-up using sparse-representations // Proceedings of the 7th International Conference on Curves and Surfaces. Berlin Heidelberg: Springer: 711–730 [DOI: 10.1007/978-3-642-27413-8_47]
  20. 20.
    Zhu Q D, Sun L and Cai C T. 2014. Non-local neighbor embedding for image super-resolution through FoE features. Neurocomputing, 141: 211–222

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