主题学习和稀疏表示的MODIS图像超分辨率重建
Super resolution reconstruction of MODIS image based on topic learning and sparse representation
- 2017年21卷第2期 页码:253-262
纸质出版日期: 2017-03
DOI: 10.11834/jrs.20176154
扫 描 看 全 文
浏览全部资源
扫码关注微信
纸质出版日期: 2017-03 ,
扫 描 看 全 文
周峰, 金炜, 龚飞, 等. 主题学习和稀疏表示的MODIS图像超分辨率重建[J]. 遥感学报, 2017,21(2):253-262.
Feng ZHOU, Wei JIN, Fei GONG, et al. Super resolution reconstruction of MODIS image based on topic learning and sparse representation[J]. Journal of Remote sensing, 2017,21(2):253-262.
针对MODIS图像分辨率受传感器限制和噪声干扰,且分辨率局限在一定水平等问题,提出一种采用主题学习和稀疏表示的MODIS图像超分辨率重建方法,该方法通过双边滤波将MODIS图像的平滑及纹理部分分离,并将纹理部分看成是由若干“文档”组成的训练样本;运用概率潜在语义分析提取“文档”的潜在语义特征,从而确定“文档”所属的“主题”。在此基础上,针对每个主题所对应的图像块,采用改进的K-SVD方法训练若干适用于不同主题的高低分辨率字典对,从而可以运用这些字典对,通过稀疏编码实现测试图像相应主题块的超分辨率重建。实验结果表明,重建图像在视觉效果和PSNR等指标上均优于传统方法。
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.
主题学习概率潜在语义分析稀疏表示超分辨率MODIS图像
topic learningprobabilistic latent semantic analysissparse representationsuper resolutionMODIS image
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
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
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
Harris J L. 1964. Diffraction and resolving power. Journal of the Optical Society of America, 54(7): 931–936
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
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]
Irani M and Peleg S. 1991. Improving resolution by image registration. CVGIP: Graphical Models and Image Processing, 53(3): 231–239
Jain A K. 1989. Fundamentals of Digital Image Processing. Englewood Cliffs, NJ: Prentice Hall
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
刘哲, 杨静, 陈路. 2015. 基于非局部稀疏编码的超分辨率图像复原. 电子与信息学报, 37(3): 522–528
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
潘宗序, 禹晶, 肖创柏, 孙卫东. 2015. 基于自适应多字典学习的单幅图像超分辨率算法. 电子学报43(2): 209–216
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
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
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]
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
沈焕锋, 李平湘, 张良培. 2006. 一种自适应正则MAP超分辨率重建方法. 武汉大学学报(信息科学版), 31(11): 949–952
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
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
Smith L N and Elad M. 2013. Improving dictionary learning: multiple dictionary updates and coefficient reuse. IEEE Signal Processing Letters, 20(1): 79–82
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]
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]
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
相关作者
相关机构