Defocus blur is one of the primary problems among hyperspectral imaging systems equipped with simple lenses. Most of the previous deblurring methods focus on how to utilize structure information of a single channel, while ignoring the characteristics of hyperspectral images. In this work, we analyze the correlations and differences among spectral channels, and propose a deblurring framework for defocus hyperspectral images. First, we divide the hyperspectral image channels into two sets, and the set with less blur is treated as a group of spectral bases. Then, according to the inherent correlations of spectral channels, a reference image can be derived from the spectral bases to guide the restoration of blurry channels. Finally, considering the disagreement between the reference image and the ground truth, a scale map based on gradient similarity is introduced as a prior in the deblurring framework. The experimental results on public dataset demonstrate that the proposed method outperforms several image deblurring methods in both visual effect and quality metrics.