An Adaptive Image Inpainting Method Based on the Weighted Mean

Nguyen Hoang Hai, Le Minh Hieu, Dang N.H. Thanh, Nguyen Van Son, Prasath V.B. Surya


Imaging inpainting is the process of digitally filling-in missing pixel values in images and requires carefully crafted image analysis tools. In this work, we propose an adaptive image inpainting method based on the weighted mean. The weighted mean is assessed to be better than the median because, for the case of the weighted mean, we can exclude the values of the corrupted pixels from evaluating values to fill those corrupted pixels. In the experiments, we implement the algorithm on an open dataset with various corrupted masks and we also compare the inpainting result by the proposed method to other similar inpainting methods – the harmonic inpainting method and the inpainting by directional median filters – to prove its own effectiveness to restore small, medium as well as fairly large corrupted regions. This comparison will be handled based on two of the most popular image quality assessment error metrics, such as the peak signal to noise ratio, and structural similarity. Further, since the proposed inpainting method is non-iterative, it is suitable for implementations to process big imagery that traditionally require higher computational costs, such as the large, high-resolution images or video sequences.

Full Text:



C. B. Schönlieb, Partial Differential Equation Methods for Image Inpainting, Cambridge: Cambridge University Press, 2015.

H. Grossauer, Digital Image Inpainting: Completion of Images with Missing Data Regions, Innsbruck: Simon & Schuster, 2008.

D. N. H. Thanh, V. B. S. Prasath, N. V. Son and L. M. Hieu, "An Adaptive Image Inpainting Method Based on the Modified Mumford-Shah Model and Multiscale parameter estimation," Computer Optics, vol. 43, no. 2, pp. 251-257, 2019.

D. N. H. Thanh, V. B. S. Prasath, L. M. Hieu and K. Hiroharu, "Image Inpainting Method Based on Mixed Median," in 2019 Joint 8th International Conference on Informatics, Electronics & Vision (ICIEV) and 2019 3rd International Conference on Imaging, Vision & Pattern Recognition (icIVPR), Spokane, 2019.

U. Erkan, S. Enginoglu and D. N. H. Thanh, "An Iterative Image Inpainting Method Based on Similarity of Pixels Values," in IEEE 6th International Conference on Electrical and Electronics Engineering (ICEEE), Istanbul, 2019.

D. N. H. Thanh, N. V. Son and V. B. S. Prasath, "Distorted Image Reconstruction Method with Trimmed Median," in IEEE 3rd International Conference on Recent Advances in Signal Processing, Telecommunications & Computing (SigTelCom), Hanoi, 2019.

D. N. H. Thanh, V. B. S. Prasath, S. Dvoenko, L. M. Hieu, "An Adaptive Image Inpainting Method Based on Euler's Elastica with Adaptive Parameters Estimation and the Discrete Gradient Method," Signal Processing, 2020 (In press).

T. F. Chan and J. Shen, Image Processing and Analysis: Variational, PDE, Wavelet and Stochastic Methods, SIAM, 2005.

L. Li, Y. Y. Ma, C. C. Chang and J. F. Lu, "Analyzing and removing SureSign watermark," Signal Processing, vol. 93, no. 5, pp. 1374-1378, 2013.

S. Masnou and J. M. Morel, "Level-lines based disocclusion," in 5th IEEE International conference on Image processing, 3:249-263, Chicago, 1998.

Z. Tauber, Z. N. Li and M. S. Drew, "Review and Preview: Disocclusion by Inpainting for Image-Based Rendering," IEEE Transactions on Systems, Man, and Cybernetics, vol. 37, no. 4, pp. 527-540, 2007.

J. Cheng and Z. Li, "Markov random field-based image inpainting with direction structure distribution analysis for maintaining structure coherence," Signal Processing, vol. 154, pp. 182-197, 2019.

D. Ding, S. Ram and J. J. Rodríguez, "Image Inpainting Using Nonlocal Texture Matching and Nonlinear Filtering," IEEE Transactions on Image Processing, vol. 28, no. 4, pp. 1705 - 1719, 2019.

H. Liu, X. Bi, G. Lu and W. Wang, "Exemplar-Based Image Inpainting With Multi-Resolution Information and the Graph Cut Technique," IEEE Access, vol. 7, pp. 101641 - 101657, 2019.

J. Shen and T. F. Chan, "Mathematical models for local nontexture inpaintings," SIAM Journal on Applied Mathematics, vol. 62, no. 3, pp. 1019-1043, 2002.

P. Zhang and F. Li, "A New Adaptive Weighted Mean Filter for Removing Salt-and-Pepper Noise," IEEE Signal Processing Letters, vol. 21, no. 10, p. 1283, 2014.

H. Noori and S. Saryazdi, "Image Inpainting Using Directional Median Filters," in IEEE International Conference on Computational Intelligence and Communication Networks, Bhopal, 2010.

D. N. H. Thanh and S. Dvoenko, "A method of total variation to remove the mixed Poisson-Gaussian noise," Pattern Recognition and Image Analysis, vol. 26, no. 2, pp. 285-293, 2016.

D. N. H. Thanh and S. Dvoenko, "Image noise removal based on Total Variation," Computer Optics, vol. 39, no. 4, pp. 564-571, 2015.

V. B. S. Prasath, D. N. H. Thanh and N. H. Hai, "On Selecting the Appropriate Scale in Image Selective Smoothing by Nonlinear Diffusion," in IEEE 7th International Conference of Communications and Electronics, pp. 267-272, Hue, 2018.

V. B. S. Prasath, D. N. H. Thanh and N. H. Hai, "Regularization Parameter Selection in Image Restoration with Inverse Gradient: Single Scale or Multiscale," in IEEE 7th International Conference on Communications and Electronics, pp. 278-282, Hue, 2018.

Z. Wang, A. Bovik, H. Sheikh, Simoncelli and Eero, "Image quality assessment: From error visibility to structural similarity," IEEE Transactions on Image Processing, vol. 13, no. 4, pp. 600-612, 2004.

U. Erkan, D. N. H. Thanh, L. M. Hieu and S. Enginoglu, "An Iterative Mean Filter for Image Denoising," IEEE Access, vol. 7, no. 1, pp. 167847-167859, 2019.

D. N. H. Thanh, V. B. S. Prasath, L. M. Hieu and S. Dvoenko, "An Adaptive Method for Image Restoration Based on High Order Total Variation and Inverse Gradient," Signal, Image and Video Processing, 2020 (In press).

D. N. H. Thanh, L. T. Thanh, N. N. Hien and V. B. S. Prasath, "Adaptive Total Variation L1 Regularization for Salt and Pepper Image Denoising," Optik - International Journal for Light and Electron Optics, 2020 (In press).

P. Jidesh and H. K. Shivarama, "Non-local total variation regularization models for image restoration," Computers & Electrical Engineering, vol. 67, pp. 114-133, 2018.

V. B. S. Prasath and D. N. H. Thanh, "Structure tensor adaptive total variation for image restoration," Turkish Journal Of Electrical Engineering & Computer Sciences, vol. 27, no. 2, pp. 1147-1156, 2019.

D. N. H. Thanh, V. B. S. Prasath, L. M. Hieu and N. N. Hien, "Melanoma Skin Cancer Detection Method Based on Adaptive Principal Curvature, Colour Normalization and Features Extraction with the ABCD Rule," Journal of Digital Imaging, 2020 (In press).


Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.