Object Removal and Image Restoration within Subspaces by Prioritized Patch Optimization

The purpose of this work is to develop a robust technique for image inpainting to restore small cracks as well as large regions those include the regions developed by object removal. The image to be restored is transformed into sub-bands by the use of DWT (Discrete Wavelet Transform). These sub-bands are reconstructed back to spatial domain to obtain the subspaces images that are at the same scale as the original image to be restored but having different resolutions. These subspace images are then subjected individually to ‘prioritized exemplar approach’ to fill-in different structures and textures simultaneously. We also optimize the patch size to cope up different sizes of textures, structures and varying resolution of the subspace images. These restored subspace images are superposed to obtain the final restored image. A number of images with changing complexion are used to estimate the effectiveness of the algorithm. The results shows visually plausible background where from the object is removed in variety of images with different structures and textures. The RMSE (Root Mean Squared Error) and PSNR (Peak Signal to Noise Ratio) measures are used to quantify the improvement over visual quality of the restoration.