IMAGE DE-BLURRING USING WIENER DE-CONVOLUTION AND WAVELET FOR DIFFERENT BLURRING KERNEL

Image de-convolution is an active research area of recovering a sharp image after blurring by a convolution. One of the problems in image de-convolution is how to preserve the texture structures while removing blur in presence of noise. Various methods have been used for such as gradient based methods, sparsity based methods, and nonlocal self-similarity methods. In this thesis, we have used the conventional non-blind method of Wiener de-convolution. Further Wavelet denoising has been used to improve the image quality without deteoriating the fine structure of images. The method has been applied for different PSF and different images to validate the results of the de-blurring