Download PDFOpen PDF in browserInverse Imaging: Reconstructing High-Resolution Images from Degraded ImagesEasyChair Preprint 78476 pages•Date: April 27, 2022AbstractImage 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, Image denoising, U-Net architecture, deep learning
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