Defocus deblurring is a classic problem in image restoration tasks. The formation of its defocus blur is related to depth. Recently, the use of dual-pixel sensor designed according to depth-disparity characteristics has brought great improvements to the defocus deblurring task. However, the difficulty of real-time acquisition of dual-pixel images brings difficulties to algorithm deployment. This inspires us to remove defocus blur by single image with depth information. We propose a single-image depth-enhanced defocus deblurring network, which uses a depth map estimated by the monocular depth estimation network to guide the network defocus deblurring. We design a deep information fusion unit, which greatly improves the effect of deblurring. Experiments show that on the single image defocus deblurring task, the experimental results demonstrate the superiority of our method.

Recent research showed that the dual-pixel sensor has made great progress in defocus map estimation and image defocus deblurring. However, extracting real-time dual-pixel views is troublesome and complex in algorithm deployment. Moreover, the deblurred image generated by the defocus deblurring network lacks high-frequency details, which is unsatisfactory in human perception. To overcome this issue, we propose a novel defocus deblurring method that uses the guidance of the defocus map to implement image deblurring. The proposed method consists of a learnable blur kernel to estimate the defocus map, which is an unsupervised method, and a single-image defocus deblurring generative adversarial network (DefocusGAN) for the first time. The proposed network can learn the deblurring of different regions and recover realistic details. We propose a defocus adversarial loss to guide this training process. Competitive experimental results confirm that with a learnable blur kernel, the generated defocus map can achieve results comparable to supervised methods. In the single-image defocus deblurring task, the proposed method achieves state-of-the-art results, especially significant improvements in perceptual quality, where PSNR reaches 25.56 dB and LPIPS reaches 0.111.

Despite supervised deep stereo matching networks have achieved impressive performance given sufficient training data, the poor generalization ability caused by the domain shifts prevents them from being applied to unseen domains. Recent progress has shown that CLIP could be a promising alternative for zero-shot visual representation task under the natural language supervision. In this paper, we present a new framework for domain generalized stereo matching by leveraging the contrastive language-image pre-training (CLIP), which distills text-guided discriminative content information rather than task-irrelevant style information. Extensive experiments show that the model generalization ability can be improved significantly in the unseen domain when transferring from SceneFlow to Middlebury, ETH3D and KITTI.