Attacking Defocus Detection With Blur-Aware Transformation for Defocus Deblurring

Previous fully-supervised defocus deblurring has made significant progress. However, training such deep models requires abundant paired ground truth, which is expensive and error-prone. This paper makes an attempt to train a defocus deblurring model without using paired ground truth and any other unpaired data. Related reblur-to-deblur schemes generally use physics-based reblur or GAN-based reblur, suffering from the robustness of blur kernel and hallucination generated by GAN. Besides, the domain gap between the realistic blurred image and reblurred image hinders deblurring performance. Addressing these challenges, we propose a weakly-supervised defocus deblurring framework via defocus detection attack. On one hand, we build a focused area detection attack (FADA) to enforce the focused area to reblur, thereby reversing its detection result by a pretrained defocus blur detection network. Moreover, we introduce a blur-aware transfer modulated from the defocused region to help FADA render a robust reblurred region. On the other hand, we implement a defocused region detection attack to guide the realistic blurred region to deblur in the process of training deblurring network with simulated-paired areas. Extensive experiments on three widely-used datasets verify the effectiveness of our framework.