Multi-Interactive Enhanced for Defocus Blur Estimation

Defocus blur estimation requires high-precision detection between the homogeneous region and transition edge. This paper develops a novel progressive design that effectively addresses this challenge. Our multi-interactive scheme could gradually learn the characteristics of degraded input and divide complex defocus blur estimation into more manageable subnetworks. Specifically, we equally degrade the source inputs and combine them with complementary information subnetworks. In the first two stages, feature interactive modules are introduced to achieve the purpose of information interaction between different features. One challenge in multi-stage networks is transmitting information features between stages, which led to the development of the supervision-guided attention module. Taking into consideration the intricacies associated with neural network design and the pronounced affinity of defocus and focus characteristics with global semantic information, in the final stage, we opt to directly input the original image, after significant affinity-based feature weighting, into the network. This strategic incorporation of global semantic information serves to mitigate the challenges posed by feature concatenation artifacts and noise encountered in the preceding two stages, thereby bolstering the accuracy of the model.