Lookahead Mask-Boundary Intervention Optimization for Diffusion-Based Image Inpainting: Predicting Downstream Boundary Coherence to Select Adaptive Blending Actions

Diffusion-based image inpainting reconstructs missing image regions by combining contextual information from known pixels with newly generated content. Despite advances in generative modeling, visible boundary artifacts, texture discontinuities, color mismatches, and unintended modifications to known regions remain important challenges. Conventional post-processing methods often employ fixed mask-blending rules, while more sophisticated inpainting systems may incorporate mask-aware features, latent-space constraints, or boundary refinement. However, the most appropriate boundary treatment may depend on the image content, mask geometry, local texture, diffusion stage, and degree of uncertainty in the generated region. This paper proposes Lookahead Mask-Boundary Intervention Optimization (LMBIO), a conceptual framework that selects boundary interventions according to their predicted downstream effects on the final inpainted image. At each selected denoising stage, an intervention generator proposes candidate boundary treatments that vary in softening width, blending strength, spatial support, and boundary-local feature fusion. A lookahead evaluator estimates the consequences of each candidate for seam visibility, texture continuity, known-region preservation, perceptual quality, and computational cost. An uncertainty-aware decision mechanism then selects the intervention with the highest predicted utility under a specified budget. The framework supports learned prediction, short-horizon simulation, and hybrid evaluation, while retaining a fixed pretrained inpainting model as the generative backbone. This paper formalizes the task, defines the intervention space and optimization objectives, presents a training strategy and inference algorithm, and specifies controlled experiments using fixed image-inpainting models and standardized masks. Comparisons against fixed blending, rule-based adaptive blending, direct policy selection, and relevant diffusion-based inpainting methods are proposed. No experimental results are claimed. The central hypothesis is that predicting the downstream consequences of boundary treatments can improve the quality-efficiency trade-off compared with selecting boundary parameters solely from fixed rules or immediate image statistics. The distinctiveness and effectiveness of this approach must be established through implementation, empirical evaluation, and comparison with existing boundary-aware inpainting research.