Conference Paper

# An efficient parameter selection criterion for image denoising

Multimedia Res. Lab., Sharif Univ. of Technol., Tehran

DOI: 10.1109/ISSPIT.2005.1577214 Conference: Signal Processing and Information Technology, 2005. Proceedings of the Fifth IEEE International Symposium on Source: IEEE Xplore

- [Show abstract] [Hide abstract]

**ABSTRACT:**Low contrast and poor quality are main problems in the production of medical images. Wavelet transform based denoising techniques are of greater interest because of their performance over Fourier and other spatial domain techniques. Selection of optimal threshold is crucial since threshold value governs the performance of denoising algorithms. For threshold selection we used normal shrink method. By using the wavelet transform and Haar transform, a novel image enhancement approach is proposed. First, a medical image was decomposed with Haar transform. then again high-frequency sub-images were decomposed .secondly noise in the frequency field was reduced by the soft-threshold method. Then high frequency coefficients are enhanced by different weight values in different sub images.Then the enhanced image was obtained through the inverse Haar transform. Lastly, the image's contrast is adjust by nonlinear contrast enhancement approaches. Experiments showed that this method can not only enhance an image's details but can also preserve its edge to increase human visibility. -
##### Conference Paper: Gaussian noise removal in gray scale images using fast Multiscale Directional Filter Banks

[Show abstract] [Hide abstract]

**ABSTRACT:**This paper presents a novel approach for Gaussian noise removal using Multiscale Filter Banks for the Contourlet Transform. The Multiscale Directional Filter Bank (MDFB) improves the radial frequency resolution of the Contourlet Transform by introducing an additional decomposition in the high frequency band. This reduces the computational complexity significantly by saving a directional decomposition because of the change in the order of decomposition. Scaling is performed by a low pass filtering based splitting and the scale decomposition is done by the Directional Filter Bank. Perfect reconstruction is possible for the scale decomposition regardless of the choice of the low pass filter. MDFB outperforms the conventional Wavelet and Contourlet transform methods for Gaussian noise removal. Denoising performance of this proposed method is compared with Wavelet and Contourlet based denoising schemes with state of art threshold methods.Recent Trends in Information Technology (ICRTIT), 2011 International Conference on; 07/2011 - [Show abstract] [Hide abstract]

**ABSTRACT:**This paper presents an overview of various threshold methods for image denoising. Wavelet transform based denoising techniques are of greater interest because of their performance over Fourier and other spatial domain techniques. Selection of optimal threshold is crucial since threshold value governs the performance of denoising algorithms. Hence it is required to tune the threshold parameter for better PSNR values. In this paper, we present various wavelet based shrinkage methods for optimal threshold selection for noise removal. General Terms Image denoising, Wavelet based methods.

Data provided are for informational purposes only. Although carefully collected, accuracy cannot be guaranteed. The impact factor represents a rough estimation of the journal's impact factor and does not reflect the actual current impact factor. Publisher conditions are provided by RoMEO. Differing provisions from the publisher's actual policy or licence agreement may be applicable.