A novel image inpainting algorithm based on sparse analysis model is proposed. The model is formulated on an analysis dictionary. The dictionary is updated using a least-squares method. The experiments on images demonstrate improved performance in peak signal to noise ratio (PSNR) compared to other image inpainting methods including AMLE Inpainting, Harmonic Inpainting, Mumford-Shah Inpainting, and Transport Inpainting algorithm. The evaluation results showed that our proposed algorithm has better performance.
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