Image denoising using new pixon representation based on fuzzy filtering and partial differential equations

Nadernejad, Ehsan; Nikpour, Mohsen

doi:10.1016/j.dsp.2012.04.016

Cited by 7 publications

(1 citation statement)

References 31 publications

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“…Overall, there are lots of problems for image denoising yet to be explored and solved. These problems should be dealt with according to the specific image noise type by using modern electronic equipment, such as spectrum analyzer, or by selecting appropriate algorithms as image denoising method [23,24] such as partial differential equations that can determine types of complicated noise. WT-ICA has become one kind of research methods for image denoising.…”

Section: Conclusion and Discussionmentioning

confidence: 99%

A New Image Denoising Method by Combining WT with ICA

Ruan

Zhao

Jia

et al. 2015

Mathematical Problems in Engineering

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In order to improve the image denoising ability, the wavelet transform (WT) and independent component analysis (ICA) are both introduced into image denoising in this paper. Although these two algorithms have their own advantages in image denoising, they are unable to reduce noises completely, which makes it difficult to achieve ideal effect. Therefore, a new image denoising method is proposed based on the combination of WT with ICA (WT-ICA). For verifying the WT-ICA denoising method, we adopt four image denoising methods for comparison: median filtering (MF), wavelet soft thresholding (WST), ICA, and WT-ICA. From the experimental results, it is shown that WT-ICA can significantly reduce noises and get lower-noise image. Moreover, the average of WT-ICA denoising image’s peak signal to noise ratio (PSNR) is improved by 20.54% compared with noisy image and 11.68% compared with the classical WST denoising image, which demonstrates its advantage. From the performance of texture and edge detection, denoising image by WT-ICA is closer to the original image. Therefore, the new method has its unique advantage in image denoising, which lays a solid foundation for the realization of further image processing task.

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Section: Conclusion and Discussionmentioning

confidence: 99%