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Inverse Imaging: Reconstructing High-Resolution Images from Degraded Images

EasyChair Preprint no. 7847

6 pagesDate: April 28, 2022

Abstract

Image Denoising simply follows the U-Net architecture which uses images with noise. In this work, we show that u-net architecture, based on convolutional and deconvolutional or transpose convolutional neural networks does a pretty good job in removing noise from the image. The task belongs to a general class of problems on Posterior probability distribution that is the probability of the parameter theta given the evidence X: P(theta | X).

Keyphrases: Convolutional Neural Networks, Deconvolutional Neural Networks, deep learning, Image denoising, U-Net architecture

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
@Booklet{EasyChair:7847,
  author = {Samir Mishra and Mrunal Chide and Aishwarya Manmode and Rohit Pandita and Mansi Bhonsle},
  title = {Inverse Imaging: Reconstructing High-Resolution Images from Degraded Images},
  howpublished = {EasyChair Preprint no. 7847},

  year = {EasyChair, 2022}}
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