These techniques are thus handicapped by their limited ability to process raw natural data such data are always huge The key is to fine tune and select the best features which is normally time consuming and not possible without considerable expertise and knowledge of the domain. READ FULL TEXT VIEW PDFĬlassical machine learning (ML) techniques are characterized by the application of the underlying algorithm to a select group of features extracted after a laborious and intelligent processing and pre-processing. Is followed by minimum overall benchmarking in the form of comparison on someĬommon dataset, while relying on the results reported in various works. Individual works, each including a short description of the method and aĬritique of the results with special reference to the benchmarking done. Relevant benchmarks are introduced in the form of datasets and state of the art Image, video and multi-dimensions, especially depth maps. We focus on the three important aspects of multimedia - namely This paper surveys the SR literature in the context ofĭeep learning. The response has been immense and in the last three years, since theĪdvent of the pioneering work, there appeared too many works not to warrant aĬomprehensive survey. Have made it inevitable for the super-resolution (SR) community to explore its The recent phenomenal interest in convolutional neural networks (CNNs) must
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