A structure sparse channel prior ( SSCP) blind image deblurring approach is presented to address the issues of inaccurate major structures and unclear edges in the blind image deblurring process. A prior method of SSCP shows that blurred images have less structured sparse channels than sharp images. Using the performance features of SSCP, it is introduced as a new regularization term into the standard deblurring model, and a novel blind deblurring model is created to achieve accurate estimation of the blur kernel. Through the coordinate descent approach alternately optimizes the latent image and blurry kernel variables. Finally, deconvolution is used to obtain deblurred clear restored images, subjective and objective comparison experiments on benchmark datasets and natural state blurred images, and application expansion experiments on human faces and low-brightness real blurred images. The experimental results show that the proposed method outperforms the classical deblurring method in terms of blur removal, structure restoration, edge retention, and visual effect by an average of 1. 72%, and the computing device designed by this method can achieve a high-precision clarity to process blurred images in the field of machine vision.