Dynamic selection of proper kernels for image deblurring: a multistrategy design

The non-blind deblurring approach can adequately deblur single-blur images by applying a suitable mathematical model. In contrast, it cannot satisfactorily deblur images that have multiple blurs. The blind deblurring approach is able to remove various kinds of blurs from an image. However, because the causes of blur in different regions differ, it is difficult to locate and remove all the blurs accurately and also to recover the fine texture details. Considering these weaknesses and strengths of both approaches, we propose a neural network that dynamically selects suitable blur kernels for deblurring. In the proposed method, the most appropriate kernels are extracted by joint training from multiple datasets that contain specific types of blurs to tackle local and global regions in one image. In addition, to further improve the image restoration quality, we designed an edge-attention mechanism to compensate the edges and structures of specific objects. The results of experiments conducted indicate that the dynamic selection of blur kernels combined with the edge attention algorithm not only improves PSNR and SSIM, but also outperforms state-of-the-art methods.