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Image Denoising using Various Wavelet Transforms: A Survey
Objectives:Image processing basically comprises of techniques employed to either enhance or restore an image. Noise may creep into the image anywhere from acquisition to transmission phase.Denoising of images can be done in spatial or frequency domain.In this paper we have compared the work done by different researchers in the domain of image restoration using wavelets. Methods/Statistical Analysis: wavelet transform has proven to be an efficient and effective method to remove noise. Researchers have explored various types of wavelets and their variations and combinations for image denoising and restoration.Performance is measured in terms of PSNR, MSE and visual quality. Many of the current techniques assume the noise model to be Gaussian. Findings:On studying work of various researchers we got to know that as level of decomposition increases performance of denoising technique improves, third and fourth level of decomposition has good results. Wavelet transform performs better than normal average filtering, gaussian filtering and wiener filters. Intra scale and interscale correlations of non orthogonal wavelet coefficients need to utilized by developing good statistical models.And thresholding process needs to be optimized that is value of threshold has to be computed with strong statistical models. Application/Improvements: As we know image processing finds application in all most all spheres of life like medical science, remote sensing, military, space exploration etc.,
Decomposition, Image Denoising, Restoration, Threshold, Wavelets.
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