Local Structural Masking for Information-Guided Image Processing

This work investigates a dense, non-semantic structural signal derived from a functional metric denoted as ϕ. The objective is to analyze the intrinsic properties of a local structural map computed directly from image data, independently of any specific downstream task, learning process, or semantic prior.

The ϕ-based map is obtained using a sliding-window formulation and produces a continuous structural response over the image domain. In this study, ϕ is treated as a functional black box, and the analysis focuses on its observable statistical and structural behavior.

To contextualize this signal, comparisons are performed with representative handcrafted baselines, including gradient-based operators (Sobel, with a scale-aligned variant) and local statistical measures (Shannon entropy computed at identical spatial scales). Robustness is evaluated under controlled perturbations such as additive noise and contrast or brightness variations.

The results show that, while correlated with classical baselines, the ϕ-based signal is not reducible to gradient magnitude or local entropy. Instead, it exhibits a distinct, scale-consistent structural behavior, supporting its interpretation as a generic structural guide for image analysis.