Improved Single-Image Based Criminisi Algorithm for Image Inpainting

This paper presents an improved Criminisi algorithm designed for single-image based image inpainting. The enhanced implementation achieves substantial improvements in computational efficiency while delivering superior restoration results. The method operates through sophisticated image analysis and processing techniques to effectively repair defects and damaged areas in digital images. Key implementation aspects include optimized priority computation for patch selection, enhanced search strategies for source patch matching, and efficient data structure utilization for faster neighborhood comparisons. The algorithm processes images by iteratively identifying the most confident patch boundaries, searching for optimal source patches from known regions, and seamlessly blending content into target areas using weighted averaging techniques. Through this advanced approach, we can more accurately reconstruct original image content and significantly enhance overall image quality. The algorithmic improvements make image inpainting more efficient and precise, representing a substantial advancement in digital image processing technology. This method effectively addresses user requirements for image restoration while providing superior visual outcomes for various applications including photographic restoration, object removal, and artifact correction.