Image denoising is one of the most pressing challenges in Image Processing. Spatial Domain Filters are long existing method for denoising that are simple and effective against different types of noises, but they do not take into account the recurring patterns. Furthermore, due to ever increasing demand for image denoising on various applications in technology, the computational and memory intensiveness along with their performance on various types of noises becomes extremely important. Recently Convolutional Neural Networks have turned out to be state-of-the-art methods for denoising. We put forward a system which integrates a deep CNN preceded by Block Matching along with traditional spatial domain methods for image denoising for both random structures of an image as well as recurring patterns. This system is evaluated over a large data set of grey-scale images and various noises and has state-of-the-art results. © BEIESP.