Abstract / truncated to 115 words (read the full abstract)

Ill-posed inverse problems appear in many signal and image processing applications, such as deblurring, super-resolution and compressed sensing. The common approach to address them is to design a specific algorithm, or recently, a specific deep neural network, for each problem. Both signal processing and machine learning tactics have drawbacks: traditional reconstruction strategies exhibit limited performance for complex signals, such as natural images, due to the hardness of their mathematical modeling; while modern works that circumvent signal modeling by training deep convolutional neural networks (CNNs) suffer from a huge performance drop when the observation model used in training is inexact. In this work, we develop and analyze reconstruction algorithms that are not restricted to a specific ... toggle 7 keywords

inverse problems image restoration (non)convex optimization sparsity deep learning back-projection objective functions


Tirer, Tom
Tel Aviv University
Publication Year
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June 27, 2023

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