Constrained least squares filtering followed by denoising of decomposed images using wave atom and wavelet transform

Image restoration is very essential in order to recover an image from its blurred and noisy form. A novel algorithm is proposed here which uses constrained least squares filter for deconvolution followed by denoising section. Deblurred image obtained after deconvolution is decomposed into texture and cartoon parts, using curvelet transform, which are then denoised using wave atom transform and wavelet transform respectively. Cycle spinning technique is used to enhance the denoising capability of wavelets and wave atoms. This algorithm does not require a priori knowledge of the noise distribution and provides effective noise reduction for texture dominant images.