The rise in portable Lidar instruments enables new opportunities for depth-assisted image processing. In this paper, we study whether the depth information provided by mobile Lidar sensors present in recent smartphones is useful for the task of image deblurring and how to integrate it with a general approach that transforms any state-of-the-art neural deblurring model into a depth-aware one. To achieve this, we developed a continual learning strategy integrating adapters into U-shaped encoder–decoder models that efficiently preprocess depth information to modulate image features with depth features. We conducted experiments on datasets with real-world depth data captured by a smartphone Lidar. The results show that our method consistently improves performance across multiple state-of-the-art deblurring baselines. Our approach achieves PSNR gains of up to 2.1 dB with a modest increase in the number of parameters, which demonstrates that utilizing true depth information can significantly boost the effectiveness of deblurring algorithms with the encoder–decoder architecture.