Infrared Image Deblurring Based on Lp-Pseudo-Norm and High-Order Overlapping Group Sparsity Regularization

A traditional total variation (TV) model for infrared image deblurring amid salt-and-pepper noise produces a severe staircase effect. A TV model with low-order overlapping group sparsity (LOGS) suppresses this effect; however, it considers only the prior information of the low-order gradient of the image. This study proposes an image-deblurring model (Lp_HOGS) based on the LOGS model to mine the high-order prior information of an infrared (IR) image amid salt-and-pepper noise. An Lp-pseudo-norm was used to model the salt-and-pepper noise and obtain a more accurate noise model. Simultaneously, the second-order total variation regular term with overlapping group sparsity was introduced into the proposed model to further mine the high-order prior information of the image and preserve the additional image details. The proposed model uses the alternating direction method of multipliers to solve the problem and obtains the optimal solution of the overall model by solving the optimal solution of several simple decoupled subproblems. Experimental results show that the model has better subjective and objective performance than Lp_LOGS and other advanced models, especially when eliminating motion blur