Hu Shaoxing, Zhang Shuyu, Zhang Aiwu, Chai Shatuo
School of Mechanical Engineering and Automation, Beihang University, Beijing 100191, China.
Ministry of Education Key Laboratory of 3D Information Acquisition and Application, Capital Normal University, Beijing 100048, China.
Sensors (Basel). 2017 Jan 3;17(1):82. doi: 10.3390/s17010082.
The spatial resolution of a hyperspectral image is often coarse as the limitations on the imaging hardware. A novel super-resolution reconstruction algorithm for hyperspectral imagery (HSI) via adaptive projection onto convex sets and image blur metric (APOCS-BM) is proposed in this paper to solve these problems. Firstly, a no-reference image blur metric assessment method based on Gabor wavelet transform is utilized to obtain the blur metric of the low-resolution (LR) image. Then, the bound used in the APOCS is automatically calculated via LR image blur metric. Finally, the high-resolution (HR) image is reconstructed by the APOCS method. With the contribution of APOCS and image blur metric, the fixed bound problem in POCS is solved, and the image blur information is utilized during the reconstruction of HR image, which effectively enhances the spatial-spectral information and improves the reconstruction accuracy. The experimental results for the PaviaU, PaviaC and Jinyin Tan datasets indicate that the proposed method not only enhances the spatial resolution, but also preserves HSI spectral information well.
由于成像硬件的限制，高光谱图像的空间分辨率通常较低。本文提出了一种基于凸集自适应投影和图像模糊度量（APOCS-BM）的高光谱图像超分辨率重建算法来解决这些问题。首先，利用基于Gabor小波变换的无参考图像模糊度量评估方法来获取低分辨率（LR）图像的模糊度量。然后，通过LR图像模糊度量自动计算APOCS中使用的边界。最后，采用APOCS方法重建高分辨率（HR）图像。通过APOCS和图像模糊度量的作用，解决了POCS中的固定边界问题，并在HR图像重建过程中利用了图像模糊信息，有效增强了空间光谱信息，提高了重建精度。对PaviaU、PaviaC和金银潭数据集的实验结果表明，该方法不仅提高了空间分辨率，而且很好地保留了高光谱图像的光谱信息。
