 This article presents a hyperspectral image deconvolution method that utilizes spectral spatial total variation prior, an explicit non-negative constraint, and spectral correlation between adjacent bands to preserve edges in the spatial domain and maintain discontinuity along the spectral dimension. The alternating direction method of multipliers, ADMM, is employed to efficiently optimize the proposed model, and a regularization parameter is updated adaptively for stability. Numerical experiments are conducted on simulated and real data to verify the performance of the proposed method. This article was authored by Hu Zhongfang, Chun Anluo, Gang Zhu, and others.