An image inpainting algorithm based on combination of wavelet transform and texture synthesis is discussed to overcome the error repair of the boundary of structure and texture in traditional image inpainting algorithm. The discussed image inpainting algorithm utilizes characters of wavelet transform domain coefficients. Wavelet transform has been used as a good image representation analysis in addition to statistical properties. Multiresolution analysis of wavelet transform is helpful to predict coarse-to-fine image structure. In particular

texture and detailed patterns for natural images must be analyzed. Wavelet can treat these elements altogether. In view of the advantages of image decomposition algorithm

wavelet coefficient statistical properties

and visual effect of edge information of an image

we proposed an image inpainting algorithm based on combination of wavelet transform and texture synthesis. Our reconstruction modeling is based on classical image decomposition model. Some actions have been taken to improve reconstruction performance. An image can be seen as a combination of texture and structure. Thus

the image repair process should fully consider the texture and structural characteristics of an image. At first

the damaged image is decomposed into low-frequency sub-image and high-frequency sub-image with different resolutions via wavelet transformation. In cases where low-frequency component represents image structure

high-frequency component reflects edge changes of an image. Moreover

low-frequency component has a positional correspondence relationship with high-frequency component. Then

sub-images are reconstructed in accordance with their respective characteristics. The sub-image that reflects structural information of an image is reconstructed with fast multipole method

whereas the sub-image that reflects texture information of an image is filled in with texture synthesis based on the characteristics of wavelet coefficient in sub-images. We introduce edge factor in combination with the characters of the wavelet transform domain coefficients to update priority function in the process of reconstituting high-frequency sub-images. Finally

the recovered sub-images are reconstructed with wavelet. Simulation results show that this hierarchical classification method works well in edge damaged blocks. The power signal-to-noise ratio of the final result compared with the traditional algorithm has been improved by approximately 1 dB to 2 dB. The repair results are consistent with human visual perception. Image decomposition model is a widely used image inpainting method. However

fuzzy and mismatching can be generated easily during the repair process. Therefore

when high-frequency component is repaired

changes in factor coefficients of high-frequency components must be introduced to enable the repair process be in accordance with edge direction. In such case

repairing image edge and improving matching block search are top priorities to reduce mismatch error. The proposed method can eliminate point defects in the repair process. Compared with the related algorithms

our algorithm holds good integrated performance. It can effectively repair damaged image with strong edges and rich texture

particularly for the loss scenarios and natural images

to improve image inpainting quality and to be consistent with human visual effects.