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Recent research showed that the dual-pixel sensor has made great progress indefocus map estimation and image defocus deblurring. However, extractingreal-time dual-pixel views is troublesome and complex in algorithm deployment.Moreover, the deblurred image generated by the defocus deblurring network lackshigh-frequency details, which is unsatisfactory in human perception. Toovercome this issue, we propose a novel defocus deblurring method that uses theguidance of the defocus map to implement image deblurring. The proposed methodconsists of a learnable blur kernel to estimate the defocus map, which is anunsupervised method, and a single-image defocus deblurring generativeadversarial network (DefocusGAN) for the first time. The proposed network canlearn the deblurring of different regions and recover realistic details. Wepropose a defocus adversarial loss to guide this training process. Competitiveexperimental results confirm that with a learnable blur kernel, the generateddefocus map can achieve results comparable to supervised methods. In thesingle-image defocus deblurring task, the proposed method achievesstate-of-the-art results, especially significant improvements in perceptualquality, where PSNR reaches 25.56 dB and LPIPS reaches 0.111.

Learnable Blur Kernel for Single-Image Defocus Deblurring in the Wild