In order to solve the problem as non-closed contour and non-uniform edge of reconstruction results in convex total variational regularization model, a mode of image deblurring based on hybrid non-convex second-order total variation and overlapping group sparse is proposed. Overlapping group sparse regularization item well considering the cross relationship between adjacent elements, non convexity second-order l p norm regularization item better keep the edge of the image shape information, and the two regular constraint into total variation method at the same time, which can accurately restore edge structure characteristics and eliminate the staircase effect and smooth ringing effect. Finally, in order to achieve the optimal solution of the non-convex higher-order model, the variable splitting method is proposed to separate the model into four sub-problems, and then the method of a re-weighted l 1 alternating direction method is used to complete the calculation of image deblurring. The test data show that compared with the current image restoration technology, the proposed algorithm has better deblurring effect, the restored image shows higher peak signal-to-noise ratio and structural similarity, which can recover edge shape information and texture details more effectively.