Context-Aware Loss Scheduling for Responsible Image Enhancement

Abstract

While modern image enhancement and restoration systems are widely used in safety-critical settings such as clinical imaging and humanitarian documentation, they tend to be context-agnostic, which is a major limitation: global quality objectives tend to only look at the picture as a whole. It does not take account of local-structure preservation and larger fidelity goals Recent works in medical enhancement put emphasis on structure preservation and non-reference evaluation to ensure that improvement does not mislead evaluations, while studies in fairness focus on differing restoration quality between groups or content types. The Context-Aware Loss Scheduling framework is an image enhancement proposal we make. CALS uses a semantic context predictor and alterations to the optimization base plate, along with an algorithm on loss scheduling aimed at contexts (e.g. medical, facial recognition/regeneration), that are iteratively executed many times to figure out how image outputs can be made higher-quality according to each context in the most pleasant or successful manner. This framework insights advance previous work on semantic-guided enhancement; preserving medical constraints and fairness-inspired evaluation techniques. Instead of making up arbitrary rules, CALS accords with these goals by contextualising the significance of objectives (retention structures, representation quality) through a weighting strategy that is dependent on context. Unlike conventional context-agnostic enhancement systems,

the proposed framework dynamically adapts objective weighting according to semantic context.  Experimental results demonstrate that context-aware scheduling reduces structural distortion, improves perceptual realism (LPIPS), and preserves identity consistency in face-sensitive scenarios. These findings support the importance of context-conditioned optimization in responsible image enhancement systems.