MATLAB Implementation of Image Inpainting Algorithm with Texture-Adaptive Features

The 2011 paper "Adaptive Image Inpainting Algorithm Based on Texture Features" presents an advanced image restoration algorithm along with corresponding simulation code. This algorithm achieves adaptive image inpainting through comprehensive analysis of texture characteristics in digital images. Key implementation aspects include texture feature extraction using methods like Gabor filters or local binary patterns, adaptive patch selection based on texture similarity metrics, and priority-based filling order calculation. The MATLAB implementation typically involves several core functions: texture analysis modules for feature mapping, priority computation functions that combine confidence and data terms, and patch matching algorithms using SSD (Sum of Squared Differences) or NCC (Normalized Cross-Correlation) for optimal source patch selection. The code structure generally follows these sequential operations: image preprocessing, damaged region identification, priority queue initialization, iterative patch-based filling, and post-processing refinement. This research made significant contributions to the image inpainting field by introducing texture-adaptive mechanisms that outperform traditional methods in handling complex textures. The provided simulation code enables researchers to easily reproduce and validate the algorithm's performance, featuring modular design that allows customization of parameters like patch size, search window dimensions, and texture sensitivity thresholds. The implementation demonstrates practical applications for restoring damaged photographs, removing objects, and reconstructing missing image regions while preserving structural and textural consistency.