Selective Image Enhancement Driven by Local Structural Information

This paper presents a selective image enhancement method guided by local structural organization rather than semantic or learned priors. A multi-scale formulation of the structural measure ϕ is introduced to reduce sensitivity to the choice of analysis scale in single-window configurations. Structural responses are aggregated across multiple spatial scales using an inter-scale consensus mechanism to guide local CLAHE enhancement.

Experimental results show that multi-scale ϕ-guided CLAHE achieves the most balanced performance among the evaluated variants, outperforming gradient- and entropy-based baselines in terms of enhancement strength, spatial confinement, and robustness. The approach is fully deterministic, non-parametric, and non-generative, making it suitable for safety-critical and embedded image processing pipelines.