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New super-resolution reconstruction method based on Mixed Sparse Representations

  • role： First author第一作者
  • Affiliation：

    Qian Xuesen Laboratory of Space Technology, China Academy of Space Technology, Beijing 100094, China

  • Email：yangxue @qxslab.cn
  • Introduction：杨雪，研究方向为图像超分辨率重建算法研究及应用。E-mail： yangxue @qxslab.cn
YANG Xue,  
  • role： Corresponding author通信作者
  • Affiliation：

    Qian Xuesen Laboratory of Space Technology, China Academy of Space Technology, Beijing 100094, China

  • Email：lifeng @qxslab.cn
  • Introduction：李峰，研究方向为图像重建、压缩感知等。E-mail： lifeng @qxslab.cn
LI Feng*,  
  • Affiliation：

    Qian Xuesen Laboratory of Space Technology, China Academy of Space Technology, Beijing 100094, China

LU Ming,  
  • Affiliation：

    Qian Xuesen Laboratory of Space Technology, China Academy of Space Technology, Beijing 100094, China

XIN Lei,  
  • Affiliation：

    Qian Xuesen Laboratory of Space Technology, China Academy of Space Technology, Beijing 100094, China

LU Xiaotian,  
  • Affiliation：

    Qian Xuesen Laboratory of Space Technology, China Academy of Space Technology, Beijing 100094, China

ZHANG Nan

Resümee

When processing remote sensing images with complex features, the conventional Super-Resolution Reconstruction (SRR) methods are often not ideal, especially for remote sensing images containing various non-uniform object information. A universal method to solve this problem is difficult to construct at present. A new SR reconstruction method of mixed sparse representation model (MSR-SRR) combined with the sparse representation and non-convex high-order total variational regularizer has been proposed to solve this problem. In this method, the sparse representation of remote sensing images in multiple transform domains is regarded as a prior probability model, and the SR reconstruction is completed by regularization. The obtained image not only retains the edge information of the image result by SR reconstruction, but also smoothens the “ladder effect” of the image. The efficiency of operation and the quality of SR reconstruction results are improved by an effective re-weighted l1 alternating direction method. Results show that the sharpness of the image increases by 31.74% on the average, the half-peak width of PSFs is the largest, and the Gaussian variance value reaches 1.8415. The GF-4 satellite images have been selected to carry out validation experiment to verify the feasibility and validity of MSR-SRR. The reconstruction results show that the images using the MSR-SRR method have better definition, richer details, and higher quality than those with non-uniform interpolation, the POCS method, and IBP method. The support vector machine method is used to classify and evaluate the accuracy of the images before and after SR reconstruction. The results show that the overall accuracy and Kappa coefficient of the reconstructed super-resolution image are improved more significantly than the original image classification results. The OA value increases by 5.96%, and the Kappa coefficient increases by 9.7%. The findings confirmed that the MSR-SRR method is effective and feasible and has extensive practical value.

Schlüsselwort

remote sensing;GF-4;Super-resolution reconstruction (SRR);Mixed sparse representation (MSR);Total variation (TV);Non-convex

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