Super-Resolution Based on Compressive Sensing and Structural Self-Similarity for Remote Sensing Images

作者：Pan, Zongxu^*; Yu, Jing; Huang, Huijuan; Hu, Shaoxing; Zhang, Aiwu; Ma, Hongbing; Sun, Weidong

来源：IEEE Transactions on Geoscience and Remote Sensing, 2013, 51(9): 4864-4876.

DOI：10.1109/TGRS.2012.2230270

• Compressive sensing (CS)
• dictionary learning
• image quality assessment (IQA)
• remote sensing image
• structural self-similarity
• super-resolution (SR)

A super-resolution (SR) method based on compressive sensing (CS), structural self-similarity (SSSIM), and dictionary learning is proposed for reconstructing remote sensing images. This method aims to identify a dictionary that represents high resolution (HR) image patches in a sparse manner. Extra information from similar structures which often exist in remote sensing images can be introduced into the dictionary, thereby enabling an HR image to be reconstructed using the dictionary in the CS framework. We use the K-Singular Value Decomposition method to obtain the dictionary and the orthogonal matching pursuit method to derive sparse representation coefficients. To evaluate the effectiveness of the proposed method, we also define a new SSSIM index, which reflects the extent of SSSIM in an image. The most significant difference between the proposed method and traditional sample-based SR methods is that the proposed method uses only a low-resolution image and its own interpolated image instead of other HR images in a database. We simulate the degradation mechanism of a uniform 2 x 2 blur kernel plus a downsampling by a factor of 2 in our experiments. Comparative experimental results with several image-quality-assessment indexes show that the proposed method performs better in terms of the SR effectivity and time efficiency. In addition, the SSSIM index is strongly positively correlated with the SR quality.

• 出版日期2013-9
• 单位首都师范大学; 北京航空航天大学; 清华大学