Narrow Results

In this paper, we present a novel method for blind image inpainting, which can restore images with missing or corrupted pixels, or images where the location of the damaged pixels is unknown. The method applies weighted nonlocal Laplacian to address the problem of blind image inpainting using low-dimensional manifold model (LDMM) regularization, and uses semi-local blocks instead of point integrals to implement constraints in LDMM. This solves the problem of low solution efficiency caused by the asymmetry of the linear equations solved by point integration, and the problem of the high iteration count to get good restoration effect. Experiments show that our method is competitive with latest methods in terms of both repairing images with large missing pixels rate and inpainting speed.

Aiming to solve the problem of blind image inpainting, this study proposed a blind image inpainting model integrated with rational fractal interpolation information. First, wavelet decomposition and closed operations were adopted to obtain masks and transform blind inpainting into non-blind inpainting. Then, on the basis of similar structural groups, rational fractal interpolation functions were introduced to complete the restoration. On the one hand, this model can sufficiently express the texture features of the image with high fidelity. On the other hand, it can better represent the structural features of the image, avoid serrated edges, enhance the restoration effect, and approximate the original image. The experimental results show that the restoration effect of this model can reserve texture details and ensure edges without distortion, possessing great practical application value.

Blind image inpainting is an approach to estimate the original image, when there is no or little knowledge of the degraded process. In this paper, the algorithm of blind image inpainting is based on edge detection methods to generate one inpainting mask H automatically. And then we combine the inpainting mask H with a TV model to get image blind inpainted. Experiment results demonstrate that the proposed algorithms is effective with application to both the synthetic and real-world images.