Original Criminisi Grayscale Image Inpainting Algorithm with Implementation

The original Criminisi grayscale image inpainting algorithm and experimental images represent a widely-used image restoration method. This algorithm automatically identifies and repairs missing regions in images, resulting in more complete and clearer restored images. The implementation typically involves key functions for patch-based texture synthesis and priority computation, where the algorithm calculates confidence terms and data terms to determine the filling order of target regions.

In our experiments, we tested the algorithm on various types of images with different defect patterns and evaluated the restoration effects through quantitative and qualitative comparisons. The MATLAB implementation includes core components such as patch matching using SSD (Sum of Squared Differences), priority queue management for efficient region filling, and isophote-driven direction computation for structure propagation.

Experimental results demonstrate that the algorithm excels at repairing defects in images, providing an effective solution for the image inpainting field. The code structure typically consists of main functions for initializing the mask, computing patch priorities, finding exemplar patches, and updating the confidence map iteratively until complete restoration is achieved.