Quality Improvement for Exemplar-based Image Inpainting using a Modified Searching Mechanism


  • Mariwan Wahid Ahmed University of Sulaimani-College of Science- Computer Department
  • Alan Anwer Abdulla Department of Information Technology, College of Commerce, University of Sulaimani, Sulaymaniyah, Iraq, 2Department of Information Technology, University College of Goizha, Sulaymaniyah, Iraq




Image Inpainting, Sum of Square Difference, Image Quality, Peak Signal-to-noise Ratio, Position Distance


Digital image processing has a significant impact in different research areas including medical image processing, biometrics, image inpainting, object detection, information hiding, and image compression. Image inpainting is a science of reconstructing damaged parts of digital images and filling-in regions in which information are missing which has many potential applications such as repairing scratched images, removing unwanted objects, filling missing area, and repairing old images. In this paper, an image inpainting algorithm is developed based on exemplar, which is one of the most important and popular images inpainting technique, to fill-in missing area that caused either by removing unwanted objects, by image compression, by scratching image, or by image transformation through internet. In general, image inpainting consists of two main steps: The first one is the priority function. In this step, the algorithm decides to select which patch has the highest priority to be filled at the first. The second step is the searching mechanism to find the most similar patch to the selected highest priority patch to be inpainted. This paper concerns the second step and an improved searching mechanism is proposed to select the most similar patch. The proposed approach entails three steps: (1) Euclidean distance is used to find the similarity between the highest priority patches which need to be inpainted with each patch of the input image, (2) the position/location distance between those two patches is calculated, and (3) the resulted value from the first step is summed with the resulted value obtained from the second step. These steps are repeated until the last patch from the input image is checked. Finally, the smallest distance value obtained in step 3 is selected as the most similar patch. Experimental results demonstrated that the proposed approach gained a higher quality in terms of both objectives and subjective compared to other existing algorithms.


[1] H. Sellahewa, S. A. Jassim and A. A. Abdulla. “Stego Quality Enhancement by Message Size Reduction and Fibonacci Bitplane Mapping”. United Kingdom, London, pp. 151-166, 2014.
[2] A. A. Abdulla. “Exploiting Similarities between Secret and Cover Images for Improved Embedding Efficiency and Security in Digital Steganography,” Department of Applied Computing, University of Buckingham, PhD Thesis, 2015.
[3] C.Guillemot and O. Meur. “Image Inpainting: Overview and Recent advances”. IEEE Signal Processing Magazine, vol. 31, no. 1, pp. 127-144, 2014.
[4] L. Cai and T.Kim. “Context-driven hybrid image inpainting”. IET Image Processing, vol. 9, no. 10, pp. 866-873, 2015.
[5] B. Nizar, H. A. Ben and M. Ali. “Automatic inpainting scheme for video text detection and removal. IEEE Transactions on Image Processing, vol. 22, pp. 4460-4472, 2013.
[6] J. K. Chhabra and V. Birchha. “An enhanced technique for exemplar based image inpainting”. International Journal of Computer Applications, vol. 115, pp. 20-25, 2015.
[7] R. H. Park and Y. Seunghwan. Red-eye detection and correction using inpainting in digital photographs”. IEEE Transactions on Consumer Electronics, vol. 55, pp. 1006-1014, 2009.
[8] M. S. Kankanhalli and W. Q. Yan. “Erasing Video Logos Based on Image Inpainting”. Vol. 2. IEEE, Lausanne, Switzerland, pp. 521-524, 2002.
[9] Wu, Y., K. Zhonglin and Z. Hongying. "An Efficient Scratches Detection and Inpainting Algorithm for old Film Restoration”. Vol. 1. IEEE, Kiev, Ukraine, pp. 75-78, 2009.
[10] Y. Mecky, G. Sergios, Y. Bin and A. Karim. “Adversarial Inpainting of Medical Image Modalities”. IEEE, Brighton, United Kingdom, pp. 3267-3271, 2019.
[11] M. B. Vaidya and K. Mahajan. “Image in painting techniques: A survey”. IOSR Journal of Computer Engineering, vol. 5, no. 4, pp. 45-49, 2012.
[12] Jain, L., A. G. Patel and K. R. Pate. "Image inpainting-a review of the underlying different algorithms and comparative study of the inpainting techniques". International Journal of Computer Applications, vol. 118, no. 10, 2015. Available from: http://www.citeseerx.ist.psu.edu/viewdoc/download?doi=
[13] B. Limbasiya and N. Pandya. “A survey on image inpainting techniques”. International Journal of Current Engineering and Technology, vol. 3, no. 5, pp. 1828-1831, 2013.
[14] P. Perez, K. Toyama and A. Criminisi. “Object Removal by Exemplar-based Inpainting”. IEEE, Madison, WI, USA, 2003.
[15] P. Perez, K. Toyama and A. Criminisi. “Region filling and object removal by exemplar-based image inpainting”. IEEE Transactions of Image Processing, vol. 13, no. 9, pp. 1200-12012, 2004.
[16] C. W. Hsieh, S. K. Lin, C. W. Wang and J. L. Wu, W. H. Cheng. “Robust Algorithm for Exemplar-based Image Inpainting”. Proceedings of International Conference Computer Graphics, Imaging and Vision, pp. 64-69, 2005.
[17] A. Wong and J. Orchard. “A Nonlocal-means Approach to Exemplar-based Inpainting”. IEEE, San Diego, CA, USA, pp. 2600-2603, 2008.
[18] T. Huang, X. Zhao and L. Deng. “Exemplar-based image inpainting using a modified priority definition”. Neurocomputing, vol. 10, no. 10, pp. 1-18, 2015.
[19] H. Liu, G. Lu, X. Wang, J. Wei, Y. Chao and X. Bi. “Exemplarbased Inpainting under Boundary Contraction Constraints”. IEEE, Shenyang, China, pp. 295-300, 2018.
[20] A. Awati, B. Pandurngi, M. R. Patil and H. C. Rao. “Image Inpainting using Exemplar Based Technique with Improvised Data Term”. IEEE, Belgaum, India, pp. 162-166, 2018.
[21] S. Wang and Y. Xu. “Image Inpainting Based on Color Differences and Structure Differences”. IEEE, Dalian, China, pp. 364-368, 2013.
[22] Available from: http://www.escience.cn/people/dengliangjian/Data. html. [Last accessed on 2019 Dec 12].