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Year of Project2013
Super-Resolution-based Inpainting IEEE Projects 2013 | Final year projects | BE Projects | Abstract: This paper introduces a new examplar-based in painting framework. A coarse version of the input image is ﬁrst in painted by a non parametric patch sampling. Compared to existing approaches, some improvements have been done (e.g. ﬁlling order computation, combination of K nearest neighbors). The in painted of a coarse version of the input image allows to reduce the computational complexity, to be less sensitive to noise and to work with the dominant orientations of image structures. From the low-resolution in painted image, a single-image super-resolution is applied to recover the details of missing areas. Experimental results on natural images and texture synthesis demonstrate the eﬀectiveness of the proposed method.
Image in painting refers to methods which consist in filling-in missing regions (holes) in an image. Existing methods can be classified into two main categories. The first category concerns diffusion-based approaches which propagate linear structures or level lines (so-called iso photes) via diffusion based on partial differential equations and variational methods. Unfortunately, the diffusion-based methods tend to introduce some blur when the hole to be filled in is large. The second family of approaches concerns exemplar-based methods which sample and copy best matching texture patches from the known image neighborhood. These methods have been inspired from texture synthesis techniques and are known to work well in cases of regular or repeatable textures. The first attempt to use exemplar-based techniques for object removal has been reported in. Authors in improve the search for similar patches by introducing an a priori rough estimate of the inpainted values using a multi-scale approach which then results in an iterative approximation of the missing regions from coarse to fine levels. The two types of methods (diffusion- and exemplar-based) can be combined efficiently, e.g. by using structure tensors to compute the priority of the patches to be filled as in.
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