Defocus blur detection based on transformer and complementary residual learning

Defocus blur detection (DBD), a technique for detecting defocus or in-focus pixels in a single image, has been widely used in various fields. Although deep learning-based methods applied to DBD attain superior performance compared to traditional methods that rely on manually-constructed features, these methods cannot distinguish many microscopic details when the images are complex. To address this issue, this work proposes a hybrid CNN-Transformer architecture (TCRL) based on complementary residual learning, which employs global information captured by the Transformer and hierarchical complementary information from the network to optimize DBD. Specifically, to enhance global target detection, our backbone network adopts a CNN-Transformer architecture, where the Transformer effectively drives the network to focus on the global context and thus achieve precise localization. To better detect microscopic details, we combine each convolutional neural network layer with layered complementary information from the network module to optimize the defocus blur detection process. This strategy opposes current schemes that output a binary mask, affording the layered feature-guided learning method to exploit better both low- and high-level information and effectively drive the network to refocus on boundaries and sparse, easily overlooked parts. Additionally, this work also considers the features of the in-focus and defocus pixels within the image. In this complementary model, the information ignored by one side may be learned by the other side, thus enhancing global target detection and local boundary refinement process. The experimental results on three datasets validate the effectiveness and superiority of the developed method.