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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

реферат

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.

ключеви́че слова́

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

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