Inpainting Process Initiates Using Segmented Image as Reference

This program implements a two-stage computational pipeline for image restoration. Initially, the algorithm performs texture-based segmentation on the input image using region-growing techniques or clustering methods (such as k-means or watershed algorithms) to partition the image into coherent regions. This segmentation enables systematic analysis of distinct image components through region-boundary detection and feature extraction. Following segmentation, the inpainting phase commences using the segmented image as a structural guide. The algorithm employs patch-based synthesis (exemplar-based inpainting) or partial differential equation methods (like Navier-Stokes based diffusion) to reconstruct missing/damaged areas. By referencing the segmentation map, the inpainting process maintains texture consistency and spatial coherence, ensuring reconstructed regions integrate seamlessly with surrounding structures through adaptive boundary matching and texture propagation algorithms. This approach preserves structural integrity while achieving visually homogeneous restoration results.