Boundary-complexity-guided adaptive weighting for single-image unsupervised superpixel segmentation

Superpixel segmentation provides an important mid-level representation for computer vision, but accurate boundary adherence remains difficult in images with weak contours, cluttered backgrounds, and complex textures. We propose a boundary-complexity-guided adaptive weighting framework for single-image unsupervised superpixel segmentation, termed AW-Super. The proposed Enhanced Boundary Feature Extraction Module (EBFEM) fuses semantic edge cues with gradient-based local structural information to produce a continuous complexity map, which is then used to reweight the optimization objective spatially. As a result, the method imposes stronger boundary-sensitive constraints in structurally complex regions while encouraging shape regularity in relatively homogeneous areas. We further introduce an adaptive stopping criterion into the Deep Image Prior optimization process, replacing a fixed iteration schedule with an image-dependent termination rule. Experiments on BSDS500 and SBD under matched superpixel budgets ( $$N=50$$ –500) demonstrate high boundary recall together with competitive overall segmentation performance, without requiring dataset-level training or ground-truth superpixel annotations. An additional cross-domain evaluation on DRIVE retinal images against SLIC and SEEDS further supports the applicability of AW-Super beyond natural-image benchmarks.