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Found 5 papers, 3 papers with code
Space-Variant Total Variation boosted by learning techniques in few-view tomographic imaging
This paper focuses on the development of a space-variant regularization model for solving an under-determined linear inverse problem.
Ambiguity in solving imaging inverse problems with deep learning based operators
Numerical experiments are performed to verify the accuracy and stability of the proposed approaches for image deblurring when unknown or not-quantified noise is present; the results confirm that they improve the network stability with respect to noise.
Image Embedding for Denoising Generative Models
Denoising Diffusion models are gaining increasing popularity in the field of generative modeling for several reasons, including the simple and stable training, the excellent generative quality, and the solid probabilistic foundation.
To be or not to be stable, that is the question: understanding neural networks for inverse problems
The solution of linear inverse problems arising, for example, in signal and image processing is a challenging problem since the ill-conditioning amplifies, in the solution, the noise present in the data.
Dissecting FLOPs along input dimensions for GreenAI cost estimations
The term GreenAI refers to a novel approach to Deep Learning, that is more aware of the ecological impact and the computational efficiency of its methods.
