Image Denoising for Gaussian Noise Reduction in Bionics Using DWT Technique
Iram Sami, Abhishek Thakur, Rajesh Kumar · 2013
This paper presents image denoising and Gaussian noise reduction model using different wavelets and combination of weiner filter along with deconvolution filters. Wavelets are the latest research area in the field of image processing and enhancement. The results show a comparison of Haar, Daubecheis and Bio-orthogonal Wavelets for Image denoising for biomedical images. The wavelet analysis represents a logical technique called as windowing technique with variable-sized regions. The wavelet analysis allows the use of shorter regions where high-frequency information is desired and the usage of shorter regions since in this domain the high-frequency information is required.. Generally biomedical image is corrupted by Gaussian noise. So image de-noising has become a very essential exercise all through the diagnosis. 2-D Discrete wavelet transform have been studied and an algorithm is developed to perform image denoising for Gaussian noise corrupted images using discrete wavelet transform. Results are both Qualitative and Quantitative analyses by obtaining the denoised version of the input image by DWT Technique and comparing it with the input image used. Quantitative analysis would be performed by checking attained Mean Square Error estimation of the denoised image. Also, estimated processing time for complete coding using different wavelets has been presented. Gaussian noise reduction is another main criterion for determining the image quality objectively. Also, another important parameter of PSF (Point Spread Function) of restored image is added to check the level of distortion in output image. In the end, a comparison table has been formulated showing the performance analysis of Haar, Db2 and Bio-orthogonal wavelets for image denoising.