Most techniques in image processing involve algorithms that are custom built and lack flexibility, making them different to the data being processed. In this chapter we elaborate upon various methodologies within the domain of image processing. We chronologically demonstrate the role of learning techniques involved in image super resolution, image upsampling, image quality assessment and parallel computing techniques. Further, an in-depth explanation is provided of the involvement of deep neural architectures as an impressive tool for solving multiple image processing problems. This chapter describes the superior performance obtained by the application of deep learning techniques to the task of image processing.