Whether the structure coherence and neighborhood consistency can be well maintained directly determines the performance of an inpainting algorithm.To achieve a better inpainting performance

this paper proposes an exemplar based image inpainting algorithm based on direction features extracted by Curvelet transform.Firstly

the super-wavelet transform is applied to extract four direction features of the corrupted image.Then the color and direction information are utilized to measure the similarities between patches.Subsequently

a color-direction structure sparsity function is defined.Afterwards

multiple suitable candidate patches are searched based on the weighted color-direction distance and these candidate patches are applied to sparsely represent target patch under the local neighborhood consistence constraints both in color and direction spaces.Moreover

in searching candidate patches

the error tolerance is adaptively decided according to the feature of target patch.Experiment results show that the proposed method can achieve better inpainted results than the state-of-the-art algorithms

especially when dealing with structure and texture images.