Wavelet Transform-Based Image Inpainting Algorithm

The wavelet transform-based image inpainting algorithm is a widely used method for image restoration. It leverages the properties of wavelet transforms to analyze and process images, effectively recovering damaged or missing portions. The algorithm operates by decomposing the image into different frequency sub-bands using wavelet decomposition functions like db4 or symlets. Each sub-band undergoes specialized inpainting processing where high-frequency sub-bands typically handle detail reconstruction while low-frequency sub-bands preserve structural information. Common implementation approaches include threshold-based coefficient modification, neighborhood similarity matching, or sparse representation techniques for sub-band restoration. After processing individual sub-bands, the algorithm reconstructs the image using inverse wavelet transform, combining the restored components to produce the final output. This method finds extensive applications in image restoration domains and effectively enhances image details and clarity while preserving critical structural information. Key implementation considerations include wavelet basis selection, threshold optimization for noise reduction, and boundary handling during the decomposition-reconstruction process.