Deep Learning has pushed the boundaries of image processing. However, that is not to say that years of advancement in image processing has become obsolete. This paper focuses on the much-debated topic of traditional imaging vs. Deep learning. The paper derives its significance by acting as an exhaustive manual that provides both quantitative and qualitative comparison between traditional imaging and modern deep learning. The aim of the paper is to guide the reader - individuals and businesses alike - to make a more informed decision when faced with the dilemma of choosing between traditional imaging or deep learning to solve a particular problem. The extensive guidelines shall help both novice developers and technical experts better assess and understand their problems, and make the most suitable choice.
In addition to the comprehensive guidelines, the advantages associated with each approach are discussed in detail too. Further, the paper also highlights some typical applications associated with each methodology. Hybrid approaches that use a selective fusion of DL and traditional Imaging techniques to offer the best of both are also discussed briefly. By delivering good accuracy while making use of optimal hardware resources, hybrid approaches pave way for future. The paper justifies this point by elaborating on some common applications where hybrid approaches excel. Download whitepaper to read more.