title: Hyperspectral Imagery Super-Resolution by Adaptive POCS and Blur Metric.
abstract: 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.
authors.affiliation: School of Mechanical Engineering and Automation, Beihang University, Beijing 100191, China. husx@buaa.edu.cn.
authors.forename: Shaoxing
authors.initials: S
authors.lastname: Hu
authors.affiliation: School of Mechanical Engineering and Automation, Beihang University, Beijing 100191, China. shuyuzhang@buaa.edu.cn.
authors.forename: Shuyu
authors.initials: S
authors.lastname: Zhang
authors.affiliation: Ministry of Education Key Laboratory of 3D Information Acquisition and Application, Capital Normal University, Beijing 100048, China. zhang_aiwu@126.com.
authors.forename: Aiwu
authors.initials: A
authors.lastname: Zhang
authors.affiliation: Qinghai Academy of Animal Science and Veterinary Medicine, Qinghai 810016, China. chaishatuo@163.com.
authors.forename: Shatuo
authors.initials: S
authors.lastname: Chai
keywords: Gabor wavelet transform; hyperspectral imagery; image blur metric; super-resolution; weighted POCS