With an unfortunate hardship in preserving the significant image content of interest, from an often contamination of noise due to evincing facts like internal element imperfections, scarce of illumination and digitization intrinsic to sensors (CCD Cameras),in addition to the environmental conditions and alignment which are extrinsic in wide variety of applications including satellite television, magnetic resonance imaging, computer tomography as well as in areas of research and technology such as geographical information systems and astronomy have lead its wings to be opened towards an evergreen application of essence in image  processing ,i.e., image denoising. Image denoising is primary task prior to any high level image processing operation, with an underlying goal to remove noise while preserving edges, is still hard striking problem, in a solution to which several algorithms with their specific assumptions, advantages and limitations have been published and  due to their inherent averaging leading to the loss of significant image features of interest in high frequency image denoising. In this paper we discuss the importance of nearly shift invariant, directional selective, dyadic decomposition tree based dual tree complex wavelet transform (DT-CWT) and an intelligent filter module (IFM) which can make the decision of selecting the filter type to denoise the image, to remove the resulting blur based on noise type and produces enthusiastic results in terms of psycho visual quality and performance metrics than those produced by previous tools and techniques.