Restoration of Noisy Blurred Images Using FFT and DWT
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Classical methods which can be used to reduce the blurring, noise or both at the same time, such as filtering and iterative methods are discussed. In this era, the need for the faster algorithm is importunate. While all classical iterative methods need iteration numbers between 50 to 100 or more wherever blur and noise increase in the images. In the proposed algorithms, a discrete wavelet transform is used to divide the image into two parts. This partition will help in increasing the manipulation speed of images that are of big sizes. The first part represents the approximation coefficients, t...
Classical methods which can be used to reduce the blurring, noise or both at the same time, such as filtering and iterative methods are discussed. In this era, the need for the faster algorithm is importunate. While all classical iterative methods need iteration numbers between 50 to 100 or more wherever blur and noise increase in the images. In the proposed algorithms, a discrete wavelet transform is used to divide the image into two parts. This partition will help in increasing the manipulation speed of images that are of big sizes. The first part represents the approximation coefficients, the blur is reduced by using the modified fixed-phase iterative algorithm recovery of blurred images. The second part represents the detail coefficients, the noise is removed by using the wavelet thresholding techniques. Many of such techniques are used like; VisuShrink, SureShrink and BayesShrink, in soft and hard thresholding. BayesShrink represents the best method because it can be used for different types of noise with excellent restored images.
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