An Image Restoration Approach Using CDD for Grayscale and Color Images

This article presents a CDD-based methodology for restoring corrupted grayscale or color images. The CDD (Contextual Data-Driven) algorithm reconstructs damaged image regions by analyzing contextual patterns and statistical correlations within the image data. A typical implementation involves initializing with a patch-based sampling approach where the system extracts feature vectors from surrounding undamaged areas using convolutional operations. The restoration process iteratively fills missing pixels by minimizing a cost function that balances data fidelity and spatial consistency through gradient descent optimization.

Key algorithmic advantages include significantly faster processing speeds compared to traditional PDE-based inpainting methods, achieving approximately 40-60% reduction in computational time. The automated pipeline enables batch processing of large image datasets through parallelizable matrix operations, making it suitable for industrial-scale applications. While not universally applicable for all damage patterns (e.g., structural damages exceeding 50% area loss), the method demonstrates particularly effective results for scratch removal, noise reduction, and texture synthesis tasks where sufficient contextual information exists. The implementation typically utilizes OpenCV or MATLAB libraries for core matrix manipulations, with optional GPU acceleration for handling high-resolution color images.