Multi-Wavelet Based Image De-noising

The Image denoising naturally corrupted by noise is a classical problem in the field of signal or image processing. Denoising of a natural images corrupted by Gaussian noise using multi-wavelet techniques are very effective because of its ability to capture the energy of a signal in few energy transfer values. Multi-wavelet can satisfy with symmetry and asymmetry which are very important characteristics in signal processing. The better denoising result depends on the degree of the noise. Generally, its energy is distributed over low frequency band while both its noise and details are distributed over high frequency band. Corresponding hard threshold used in different scale high frequency sub-bands. This work is proposed to indicate the suitability of different wavelet and multi-wavelet based and a size of different neighborhood on the performance of image denoising algorithm in terms of PSNR value. Finally it compares wavelet and multi-wavelet techniques and produces best denoised image using multi-wavelet technique based on the performance of image denoising algorithm in terms of PSNR Values.

Autorentext

Manuraj Jaiswal is working as an Assistant Professor in the department of Computer Science and Engineering at RSRRCET,CSVTU, Bhilai, India. His research interests include image processing and applications such as Multi-wavelets, biomedical imaging, neural network.