Please use this identifier to cite or link to this item: http://drsr.daiict.ac.in//handle/123456789/1014
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dc.contributor.advisorKumar, Ahlad
dc.contributor.authorSingh, Harsh Vardhan
dc.date.accessioned2022-05-06T17:36:06Z
dc.date.available2023-02-24T17:36:06Z
dc.date.issued2021
dc.identifier.citationSingh, Harsh Vardhan (2021). Image Super Resolution Using Deep Neural Networks. Dhirubhai Ambani Institute of Information and Communication Technology. viii, 46 p. (Acc.No: T00949)
dc.identifier.urihttp://drsr.daiict.ac.in//handle/123456789/1014
dc.description.abstractThe recent outbreak of COVID-19 has motivated researchers to contribute in the area of medical imaging using artificial intelligence and deep learning. Superresolution (SR), in the past few years, has produced remarkable results using deep learning methods. The ability of deep learning methods to learn the non-linear mapping from low-resolution (LR) images to their corresponding high-resolution (HR) images leads to compelling results for SR in diverse areas of research. In this paper, we propose a deep learning based image super-resolution architecture in Tchebichef transform domain. This is achieved by integrating a transform layer into the proposed architecture through a customized Tchebichef convolutional layer (TCL). The role of TCL is to convert the LR image from the spatial domain to the orthogonal transform domain using Tchebichef basis functions. The inversion of the aforementioned transformation is achieved using another layer known as the Inverse Tchebichef convolutional Layer (ITCL), which converts back the LR images from the transform domain to the spatial domain. It has been observed that using the Tchebichef transform domain for the task of SR takes the advantage of high and low-frequency representation of images that makes the task of super-resolution simplified. We, further, introduce transfer learning approach to enhance the quality of Covid based medical images. It is shown that our architecture enhances the quality of X-ray and CT images of COVID-19, providing a better image quality that helps in clinical diagnosis. Experimental results from our architecture provides competitive results when compared with most of the deep learning methods employed using a fewer number of trainable parameters.
dc.subjectDeep Learning
dc.subjectImage Super Resolution
dc.subjectTchebichef Moments
dc.subjectConvolutional Neural Network
dc.classification.ddc681.2 SIN
dc.titleImage Super Resolution Using Deep Neural Networks
dc.typeDissertation
dc.degreeM. Tech
dc.student.id201911022
dc.accession.numberT00949
Appears in Collections:M Tech Dissertations

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