为了进一步提高遥感图像超分辨效果, 提高超分辨重建速度。针对以往稀疏超分辨算法中更容易丢失边缘信息和引入噪声的问题, 本文改进了特征提取算子, 以对称近邻滤波(SNN)代替高斯滤波, 重点解决特征空间中的字典学习问题。首先, 根据遥感图像退化模型生成训练样本图像, 并分别对高、低分辨率遥感图像进行7×7分块, 生成字典训练样本。然后, 建立连接高、低分辨率图像空间的双参数联合稀疏字典, 将字典学习过程中的稀疏系数分解为系数权值和字典原子的乘积, 依据字典原子指标训练和更新字典, 得到高低分辨率联合字典映射矩阵。最后, 进行遥感图像超分辨稀疏重构。实验结果表明: 与当前最先进的稀疏表示超分辨算法相比, 本文算法得到的超分辨重建遥感图像的主观效果更好, 恢复出更多的地物细节信息; 客观评价参数峰值信噪比(PSNR)提高约1.7 dB, 结构相似性(SSIM)提高约0.016。改进的稀疏表示超分辨算法可以有效地提高遥感图像超分辨效果, 同时降低重建时间。

To solve the problems of lost details and added noise in the previous sparse representation image super-resolution, an improved feature extraction algorithm was proposed to improve the image Super-Resolution Reconstruction (SRR) effect. The Gaussian filter was replaced by a symmetric nearest neighbor filter to speed up image super-resolution, and the problem of dictionary learning in the feature space was solved. First, sample training images were generated based on the remote sensing image degradation model, and high-low resolution images were divided into image patches sized 7×7. Then, a high-low resolution joint dictionary mapping matrix was generated after the dictionary was trained and updated. Finally, image super-resolution reconstruction was performed in sparse representation. Experimental results revealed that the proposed method reconstructed a higher-quality super-resolution image in less time. Simultaneously, as compared with the image obtained with the most advanced sparse representation super-resolution algorithm, the SRR resulting image contained more texture details of ground objects. In addition, the peak signal-to-noise ratio and structural similarity index measure were increased by approximately 1.7 dB and 0.016, respectively. Conclusion: The improved sparse representation SRR algorithm can effectively improve the SRR effect of remote sensing images and reduce the super-resolution reconstruction time.