Criminisi Classic Algorithm Implementation and Analysis

In this article, we introduce the classic implementation of the Criminisi algorithm. This algorithm is one of the commonly used methods in image inpainting, particularly effective in removing image noise and reconstructing missing regions. The core mechanism utilizes surrounding pixel information to predict and reconstruct missing areas through priority-based patch filling. For beginners, this classic implementation provides significant learning support through well-commented code structure. The algorithm typically involves three key computational steps: calculating patch priorities based on confidence and data terms, identifying optimal source patches using SSD (Sum of Squared Differences) matching, and updating confidence values after each filling iteration. The provided code examples demonstrate proper handling of isophote-driven diffusion and exemplar-based synthesis. The article includes pre-configured test images that facilitate a comprehensive learning path from basic understanding to practical mastery of the Criminisi algorithm. These resources help readers better comprehend the implementation methodology and application scenarios through hands-on experimentation. The implementation showcases proper boundary handling and priority queue management using efficient data structures. Furthermore, we will examine the algorithm's advantages and limitations, including its computational efficiency in handling large missing regions and potential challenges with textured backgrounds. Real-world application cases such as artifact restoration and object removal will be discussed, enabling readers to gain deeper insights into this classical algorithm's practical implementations and optimization techniques.