A novel image denoising method based on force field transform

Yan Zhang; Kun Gao; Guoqiang Ni; Tingzhu Bai

doi:10.1117/12.807031

28 January 2009 A novel image denoising method based on force field transform

Yan Zhang, Kun Gao, Guoqiang Ni, Tingzhu Bai

Author Affiliations +

Proceedings Volume 7156, 2008 International Conference on Optical Instruments and Technology: Optical Systems and Optoelectronic Instruments; 71562D (2009) https://doi.org/10.1117/12.807031
Event: International Conference of Optical Instrument and Technology, 2008, Beijing, China

Abstract

As a fundamental image processing operation, a good denoising method should keep the original image information as much as possible. However, most denoising methods may degrade or remove the fine details and texture of the original image. In this paper, a force field method is adopted to transform the image pixels within a local window into a potential energy surface and then to distinguish the image edges and the noises in this potential energy field. Afterwards, different templates are used according to the judgment and the adaptive filter is applied to the local pixels respectively. This new method has less computational complexity than the other algorithms of transform domain, which means it can be implemented in a real-time processing system. Also the new method can preserve more image edges than the traditional filters. Finally the performance of the proposed method is compared in this paper with other popular methods by using evaluation criterion of SNR and SSIM(a measure of structural similarity). The results show that the proposed method is reliable and especially helpful to preserve the image details.

Citation Download Citation

Yan Zhang, Kun Gao, Guoqiang Ni, and Tingzhu Bai "A novel image denoising method based on force field transform", Proc. SPIE 7156, 2008 International Conference on Optical Instruments and Technology: Optical Systems and Optoelectronic Instruments, 71562D (28 January 2009); https://doi.org/10.1117/12.807031

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KEYWORDS

Signal to noise ratio

Digital filtering

Image quality

Image filtering

Denoising

Image denoising

Wavelets

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