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Deep Image Prior

Deep Image Prior

A system for image enhancement and restoration

Deep Image Prior is an image enhancement and restoration system without neural network training. It uses a neural network as a prior for image processing task, and is used to ultimately restore or reconstruct images inversely.



The structure of the network is sufficient to capture low-level image statistics prior to any learning contrary to the common practice of training a deep neural network with a dataset. It uses randomly initialized neural network handcrafted priors to address images issues such as denoising, super-resolution, in-painting and restore images based on flash-no flash input pairs.



It also highlights the inductive bias captured by standard generator network architectures and bridges the gap between two very popular families of image restoration methods: learning-based methods using deep convolutional neural networks and learning-free methods based on handcrafted image priors.



Learned-prior and explicit-prior are the two most widely used forms of image restoration. Learned-prior involves training a deep convolution network to study the given world in the image through a dataset, allowing it to remove noise from the image. Explicit-prior is when a network is taught what an image is supposed to look like by being given types of images that are natural.

Timeline

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Further reading

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Deep Image Prior

Dmitry Ulyanov, Andrea Vedaldi and Victor Lempitsky

Academic paper



Deep Image Prior Supplementary Material

Dmitry Ulyanov, Andrea Vedaldi and Victor Lempitsky

Academic paper



Documentaries, videos and podcasts

Title
Date
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Deep Image Prior | Two Minute Papers #219

10 January 2018

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References