基于小波域压缩感知的遥感图像超分辨算法

Super-resolution algorithm for remote sensing images based on compressive sensing in wavelet domain

针对单字典表达复杂多样的图像纹理存在一定的局限性的问题,利用压缩感知和小波理论建立了一种多字典遥感图像超分辨算法。首先,对训练图像在小波域的不同频带利用K-奇异值分解(K-SVD)算法建立不同的字典;然后,利用全局限制求取高分辨率图像的初始解;最后,利用正交匹配追踪算法(OMP)对初始解在小波域进行多字典稀疏求解。实验结果表明,相比基于单字典的超分辨重建算法,结果图像的主观视觉效果有很大提高,客观评价指标的峰值信噪比(PSNR)和结构相似度(SSIM)分别提高2.8 dB以上和0.01以上。字典可一次建立重复使用,降低了运算时间。

Focused on the issue that complex image texture can not be fully expressed by single dictionary in image Super-Resolution （SR） reconstruction, a remote sensing image super-resolution algorithm based on compressive sensing and wavelet theory using multiple dictionaries was proposed. Firstly, the K-Singular Value Decomposition （K-SVD） algorithm was used to establish the different dictionaries in the different frequency bands in wavelet domain. Secondly, the initial solution of SR image was obtained by using global limited condition. Finally, the sparse solution of multiple dictionaries in wavelet domain was implemented using Orthogonal Matching Pursuit （OMP） algorithm. The experimental results show that the proposed algorithm presents the better subjective visual effect compared with the single dictionary based algorithm. The Peak Signal-to-Noise Ratio （PSNR） and the Structural SIMilarity （SSIM） index increase more than 2.8 dB and O. 01 separately. The computation time is reduced as the dictionaries can be used once again.