NUN: Nested Unfolding Network for Real-World Concealed Object Segmentation

Real-world visual perception demands joint handling of low-level image degradation and high-level semantic understanding, yet thesetwo objectives inherently conflict: restoration seeks fine-grained texturerecovery while segmentation prioritizes semantic contrast. We proposethe nested unfolding network (NUN), a principled framework that re-solves this conflict by nesting one deep unfolding network (DUN) in-side another. NUN embeds a degradation-resistant unfolding network(DeRUN) within each stage of a segmentation-oriented unfolding network(SODUN), enabling both tasks to optimize in their own subspaces whileinteracting in a controlled manner. DeRUN handles unknown degrada-tion through proximal gradient unfolding with learnable operators thatimplicitly approximate it, while SODUN performs reversible foreground-background estimation. A bi-directional unfolding interaction mechanismuses IQA to select optimal DeRUN outputs, and a cross-stage consis-tency loss ensures robust predictions under varying restoration quality.Theoretically, under local assumptions, we show that NUN reduces di-rect parameter-level gradient conflict through disjoint parameter setsand achieves a degradation-sensitivity bound, where degradation affectssegmentation only through the inner-loop optimization and restorationapproximation errors. We instantiate NUN on concealed object segmen-tation and demonstrate consistent superiority over SOTA alternativesacross 12 benchmarks. The code is available at https://github.com/ChunmingHe/NUN.