Image Inpainting Methods Evaluation and Improvement

With the upgrowing of digital processing of images and film archiving, the need for assisted or unsupervised restoration required the development of a series of methods and techniques. Among them, image inpainting is maybe the most impressive and useful. Based on partial derivative equations or texture synthesis, many other hybrid techniques have been proposed recently. The need for an analytical comparison, beside the visual one, urged us to perform the studies shown in the present paper. Starting with an overview of the domain, an evaluation of the five methods was performed using a common benchmark and measuring the PSNR. Conclusions regarding the performance of the investigated algorithms have been presented, categorizing them in function of the restored image structure. Based on these experiments, we have proposed an adaptation of Oliveira’s and Hadhoud’s algorithms, which are performing well on images with natural defects

Image inpainting using LLE-LDNR and linear subspace mappings

International audienceThe paper first describes an examplar-based image inpainting algorithm using a locally linear neighbor embedding technique with low-dimensional neighborhood representation (LLE-LDNR). The inpainting algorithm first searches the K nearest neighbors (K-NN) of the input patch to be filled-in and linearly combine them with LLE-LDNR to synthesize the missing pixels. Linear regression is then introduced for improving the K-NN search. The performance of the LLE-LDNR with the enhanced K-NN search method is assessed for two applications: loss concealment and object removal