A novel method was proposed to enhance the quality of blurred infrared images captured during unmanned aerial vehicle (UAV) inspections of oil and gas pipelines. The issue of image deblurring was addressed by utilizing prior knowledge of image channels and employing bilateral filtering and the non-blind deconvolution network (NBDN) to remove artificial artifacts. Firstly, the dark channel prior knowledge was incorporated into a maximum a posteriori optimization framework by adding a dark channel L₀ regularization term. Then, instead of using L₀ regularization on image pixels, the L₀ regularization term based on image gradients was employed as the constraint for the latent image. The blur kernel and the intermediate latent image were iteratively estimated through alternating estimation techniques and indirect optimization methods including semi-quadratic splitting and table lookup. The blur kernel was estimated using bilinear interpolation, and an image pyramid was constructed by upsampling and downsampling the image, which were then directly optimized by the conjugate gradient method. Finally, with the estimated blur kernel, a non-blind deblurring method based on the super-Laplacian prior was presented to obtain the latent imageI₁, while another non-blind deblurring method based on L₀ regularization was also applied to obtain the latent image I₀. The difference map between I₁ and I₀ was calculated and then subtracted from I₁ by bilateral filtering to obtain the final latent image I. The experiments were designed on low-light images, images with saturated pixels, real images, and infrared camera images to asses the proposed algorithm. The results show that the proposed method has strong competitiveness in various blurry image restoration effects.