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Title: :  low resolution images to high resolution images
PaperId: :  14550
Published in:   International Journal Of Advance Research And Innovative Ideas In Education
Publisher:   IJARIIE
e-ISSN:   2395-4396
Volume/Issue:    Volume 7 Issue 3 2021
DUI:    16.0415/IJARIIE-14550
Licence: :   IJARIIE is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Author NameAuthor Institute
Roushan kumarDy patil college of engineering pune
Aryan jainDy patil college of engineering pune
ashish vermaDy patil college of engineering pune
Arushi rainaDy patil college of engineering pune
Bhavika pareekDy patil college of engineering pune

Abstract

information technology
generative adverserial network,deep learning,super resolution.
Image Super resolution is a widely-studied problem in computer vision, where the objective is to convert a lowresolution image to a high resolution image. Conventional methods for achieving super-resolution such as image priors, interpolation, sparse coding require a lot of pre/post processing and optimization. Recently, deep learning methods such as convolutional neural networks and generative adversarial networks are being used to perform super-resolution with results competitive to the state of the art but none of them have been used on microscopy images. In this thesis, a generative adversarial network, mSRGAN, is proposed for super resolution with a perceptual loss function consisting of a adversarial loss, mean squared error and content loss. The objective of our implementation is to learn an end to end mapping between the low / high resolution images and optimize the upscaled image for quantitative metrics as well as perceptual quality. We then compare our results with the current state of the art methods in super resolution, conduct a proof of concept segmentation study to show that super resolved images can be used as a effective pre processing step before segmentation and validate the findings statistically.In most digital imaging applications, high-resolution images are preferred and often required to accomplish tasks. Image super-resolution (SR) is a widely-studied problem in computer vision, where the objective is to generate one or more highresolution images from one or more low-resolution images. SR algorithm aims to produce details finer than the sampling grid of a given imaging device by increasing the number of pixels per unit area in an image.

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IJARIIE Roushan kumar, Aryan jain, ashish verma, Arushi raina, and Bhavika pareek. "low resolution images to high resolution images" International Journal Of Advance Research And Innovative Ideas In Education Volume 7 Issue 3 2021 Page 2044-2050
MLA Roushan kumar, Aryan jain, ashish verma, Arushi raina, and Bhavika pareek. "low resolution images to high resolution images." International Journal Of Advance Research And Innovative Ideas In Education 7.3(2021) : 2044-2050.
APA Roushan kumar, Aryan jain, ashish verma, Arushi raina, & Bhavika pareek. (2021). low resolution images to high resolution images. International Journal Of Advance Research And Innovative Ideas In Education, 7(3), 2044-2050.
Chicago Roushan kumar, Aryan jain, ashish verma, Arushi raina, and Bhavika pareek. "low resolution images to high resolution images." International Journal Of Advance Research And Innovative Ideas In Education 7, no. 3 (2021) : 2044-2050.
Oxford Roushan kumar, Aryan jain, ashish verma, Arushi raina, and Bhavika pareek. 'low resolution images to high resolution images', International Journal Of Advance Research And Innovative Ideas In Education, vol. 7, no. 3, 2021, p. 2044-2050. Available from IJARIIE, https://ijariie.com/AdminUploadPdf/low_resolution_images_to_high_resolution_images_ijariie14550.pdf (Accessed : ).
Harvard Roushan kumar, Aryan jain, ashish verma, Arushi raina, and Bhavika pareek. (2021) 'low resolution images to high resolution images', International Journal Of Advance Research And Innovative Ideas In Education, 7(3), pp. 2044-2050IJARIIE [Online]. Available at: https://ijariie.com/AdminUploadPdf/low_resolution_images_to_high_resolution_images_ijariie14550.pdf (Accessed : )
IEEE Roushan kumar, Aryan jain, ashish verma, Arushi raina, and Bhavika pareek, "low resolution images to high resolution images," International Journal Of Advance Research And Innovative Ideas In Education, vol. 7, no. 3, pp. 2044-2050, May-Jun 2021. [Online]. Available: https://ijariie.com/AdminUploadPdf/low_resolution_images_to_high_resolution_images_ijariie14550.pdf [Accessed : ].
Turabian Roushan kumar, Aryan jain, ashish verma, Arushi raina, and Bhavika pareek. "low resolution images to high resolution images." International Journal Of Advance Research And Innovative Ideas In Education [Online]. volume 7 number 3 ().
Vancouver Roushan kumar, Aryan jain, ashish verma, Arushi raina, and Bhavika pareek. low resolution images to high resolution images. International Journal Of Advance Research And Innovative Ideas In Education [Internet]. 2021 [Cited : ]; 7(3) : 2044-2050. Available from: https://ijariie.com/AdminUploadPdf/low_resolution_images_to_high_resolution_images_ijariie14550.pdf
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