In the development of imaging science and image processing request in our daily life, inpainting large regions always plays an important role. However, the existing local regularized models and some patch manifold based non-local models are often not effective in restoring the features and patterns in the large missing regions. In this paper, we will apply a strategy of inpainting from outside to inside and propose a re-weighted matching algorithm by closest patch (RWCP), contributing to further enhancing the features in the missing large regions. Additionally, we propose another re-weighted matching algorithm by distance-based weighted average (RWWA), leading to a result with higher PSNR value in some cases. Numerical simulations will demonstrate that for large region inpainting, the proposed method is more applicable than most canonical methods. Moreover, combined with image denoising methods, the proposed model is also applicable for noisy image restoration with large missing regions.
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Large region ipainting by some canonical and recent methods
An example of similar patches in fruits image
Large region inpainting for a fruits image
Convergence curve for the object functions in Algorithm 2. The left image is the convergence curve for RWMC model and the right image is that for RWWA model
The result of large region inpainting from noisy images with Gaussian noise
Numerical results for boat and bricks image inpainting from missing large region
Edge detection of inpainting results via Canny operator
Large region inpainting from images with Gaussian noise by Algorithm 3