BSCB Image Inpainting Algorithm Implementation

This implementation presents a MATLAB-based BSCB image inpainting algorithm that has been thoroughly tested and successfully processes both grayscale and color images. The algorithm employs the BSCB (Bertalmio-Sapiro-Caselles-Ballester) model, which utilizes partial differential equations (PDEs) to propagate image information from surrounding areas into damaged regions. The implementation involves several key computational stages: initial detection of damaged areas using mask identification, followed by iterative propagation of intensity values along isophote directions (image gradients) to maintain structural continuity. For color images, the algorithm processes each RGB channel separately while maintaining color consistency through vector-valued PDE formulations. Core MATLAB functions include gradient computation using finite differences, anisotropic diffusion filters for noise removal, and iterative pixel restoration through Euler numerical schemes. The algorithm effectively handles missing pixel reconstruction while preserving edge sharpness and texture details through curvature-driven diffusion mechanisms. This image restoration approach demonstrates significant applications in computer vision and digital image processing, particularly for reconstructing damaged photographs, removing occlusions, and enhancing visual quality in archival image restoration. The method excels in recovering fine details and color fidelity, producing results with improved clarity and natural appearance compared to conventional interpolation methods.