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Automatically improving image quality using tensor voting

  • ICONIP2009
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Abstract

A novel corrupted region detection technique based on tensor voting is proposed to automatically improve the image quality. This method is suitable for restoring degraded images and enhancing binary images. First, the input images are converted into layered images in which each layer contains objects having similar characteristics. By encoding the pixels in the layered images with second-order tensors and performing voting among them, the corrupted regions are automatically detected using the resulting tensors. These corrupted regions are then restored to improve the image quality. The experimental results obtained from automatic image restoration and binary image enhancement applications show that our method can successfully detect and correct the corrupted regions.

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Acknowledgments

This research was supported by the MKE (The Ministry of Knowledge Economy), Korea, under the ITRC (Information Technology Research Center) support program supervised by the NIPA (National IT Industry Promotion Agency) (NIPA-2010-C1090-1011-0008). The corresponding authors are Jonghyun Park (Ph. D.) and Gueesang Lee (Ph. D.).

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Authors and Affiliations

  1. Department of Electronics and Computer Engineering, Chonnam National University, Gwangju, Korea

    Toan Dinh Nguyen, Jonghyun Park, Soohyung Kim, Hyukro Park & Gueesang Lee

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  1. Toan Dinh Nguyen
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  2. Jonghyun Park
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  3. Soohyung Kim
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  4. Hyukro Park
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  5. Gueesang Lee
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Correspondence to Gueesang Lee.

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Nguyen, T.D., Park, J., Kim, S. et al. Automatically improving image quality using tensor voting. Neural Comput & Applic 20, 1017–1026 (2011). https://doi.org/10.1007/s00521-010-0394-4

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  • DOI: https://doi.org/10.1007/s00521-010-0394-4

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