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In this article, the concept of ensemble learning and its usage in deep learning for super resolution is discussed with the help of one of its applications in the paper ‘MRI Super-Resolution with Ensemble Learning and Complementary Priors’ as an example.
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This is the second part of the Super resolution series. It’s mainly about supervised superresolution and an introdution to frameworks used in this domain.
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This article is mainly aimed at Machine Learning and Computer Vision enthusiasts with little or no background in these fields. In this part, I briefly introduced the concept of Super-Resolution and its confounding applications in various fields. I also discussed a few initial algorithms which paved the way to current progress in this domain.
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In this article, the highlights of EnhanceNet paper for single image super-resolution and the performance of various versions of it are discussed. I would like to mainly emphasize its loss functions which are responsible for a perceptual quality close to the original high-resolution image.