This study aims to overcome the challenges of traditional image inpainting algorithms

wherein texture extension may occur and some incorrect samples may be selected as candidate patches when sample-based algorithms are employed to inpaint the damaged region with complex geometric structure and rich texture. An image inpainting method based on weight variation of neighborhood window is proposed. In the method

so-called weight variation is introduced by combining total variation and intrinsic variation in a neighborhood window to modify the priority measure in Criminisi's algorithm. With the proposed method

the ability of identifying geometric and texture information has been improved and geometric information can be urgently inpainted. Meanwhile

matching accuracy has been improved by introducing structure difference operator in combination with pixel color comparison. Compared with other recent algorithm

the proposed algorithm can settle the problem of texture expansion and block mismatching and can maintain visual connectivity. Moreover

the peak signal-to-noise ratio(PSNR) of its inpainting result is improved by 2 dB to 3 dB. Compared with the original Criminisi's algorithm and its improved algorithms

the proposed algorithm can achieve better result in inpainting the damaged region with both geometric structure and rich texture

as well as in inpainting some ordinary damaged region. Thus

the proposed algorithm has generality.