  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.
