In this paper, we propose a method to enhance the quality of blurred infrared images captured during unmanned aerial vehicle (UAV) inspections of oil and gas pipelines. We address the issue of image deblurring by utilizing prior knowledge of image channels and employing bilateral filtering and the Non-Blind Deconvolution Network (NBDN) to remove artificial artifacts. Firstly, we incorporate the dark channel prior knowledge into a maximum a posteriori optimization framework by adding a dark channel regularization term. Then, instead of using regularization on image pixels, we utilize an regularization term based on image gradients as a regularization constraint for the latent image. We iteratively estimate the blur kernel and the intermediate latent image using alternating estimation techniques and indirect optimization methods such as semi-quadratic splitting and table lookup. The blur kernel is estimated using bilinear interpolation, and an image pyramid is constructed by upsampling and downsampling the image, which is then directly optimized using the conjugate gradient method. Finally, with the estimated blur kernel, we employ a non-blind deblurring method based on the super-Laplacian prior to obtain the latent image, and another non-blind deblurring method based on regularization to obtain the latent image . We calculate the difference map between the estimated latent images and and subtract the filtered difference map from using bilateral filtering to obtain the final latent image I.We conducted experiments on low-light images, images with saturated pixels, real images, and infrared camera images to evaluate the proposed algorithm. The experimental results demonstrated the competitive performance of our method compared to other image deblurring algorithms in terms of restoring various types of blurred images.