Automatically improving image quality using tensor voting

Authors:
Toan Dinh Nguyen;Jonghyun Park;Soohyung Kim;Hyukro Park;Gueesang Lee
Affiliations:
Chonnam National University, Department of Electronics and Computer Engineering, Gwangju, Korea;Chonnam National University, Department of Electronics and Computer Engineering, Gwangju, Korea;Chonnam National University, Department of Electronics and Computer Engineering, Gwangju, Korea;Chonnam National University, Department of Electronics and Computer Engineering, Gwangju, Korea;Chonnam National University, Department of Electronics and Computer Engineering, Gwangju, Korea
Venue:
Neural Computing and Applications - Special Issue on ICONIP2009
Year:
2011

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Automatic restoration of corrupted regions in images using object width transform

Proceedings of the 6th International Conference on Ubiquitous Information Management and Communication

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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.