SAS: A General Framework Induced by Sequence Association for Shape from Focus

Shape from focus (SFF) is a technique used to estimate the depth of a scene from a sequence of multifocus images. Existing SFF methods can be categorized into two groups: traditional methods and deep learning-based methods. Traditional methods typically employ a focus measure (FM) operator to assess the sharpness of individual pixels in a single-frame image, often overlooking the associations within the image sequence. Deep learning methods generally rely on labeled datasets, which are often challenging to obtain in real-world scenarios. Based on these observations, we propose a novel sequence association-based (SAS) framework aimed at enhancing the generalizability of SFF methods. In the SAS framework, an image sequence is treated as complete three-dimensional (3D) data throughout the processes of multiview decomposition, selective fusion and multiscale feature aggregation. Furthermore, the framework includes a tighter multiview learning generalization error bound to guide the development of the selective fusion method. This method leverages isomorphisms among multiple views to effectively mitigate the adverse effects of outlier noise on the reconstruction of various scenes. Comprehensive experiments on seven synthetic datasets and two real scenes with unknown labels demonstrate the effectiveness and generalizability of the SAS framework compared to state-of-the-art SFF methods.