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Concept

Deep image prior

Related publications (29)

Towards Trustworthy Deep Learning for Image Reconstruction

Alexis Marie Frederic Goujon

The remarkable ability of deep learning (DL) models to approximate high-dimensional functions from samples has sparked a revolution across numerous scientific and industrial domains that cannot be overemphasized. In sensitive applications, the good perform ...

EPFL2024

The Halos and Environments of Nearby Galaxies (HERON) survey IV. Complexity in the boxy galaxies NGC720 and NGC2768

Oliver Müller

The shapes of galaxies, their outer regions in particular, are important guideposts to their formation and evolution. In this work, we report on the discovery of strongly box-shaped morphologies of the otherwise well-studied elliptical and lenticular galax ...

Edp Sciences S A2024

Towards learning-based denoising of light fields

Touradj Ebrahimi, Michela Testolina

In recent years, new emerging immersive imaging modalities, e.g. light fields, have been receiving growing attention, becoming increasingly widespread over the years. Light fields are often captured through multi-camera arrays or plenoptic cameras, with th ...

2023

Bayes-optimal Learning of Deep Random Networks of Extensive-width

Lenka Zdeborová, Florent Gérard Krzakala, Hugo Chao Cui

We consider the problem of learning a target function corresponding to a deep, extensive-width, non-linear neural network with random Gaussian weights. We consider the asymptotic limit where the number of samples, the input dimension and the network width ...

2023

The autoregressive neural network architecture of the Boltzmann distribution of pairwise interacting spins systems

Indaco Biazzo

Autoregressive Neural Networks (ARNNs) have shown exceptional results in generation tasks across image, language, and scientific domains. Despite their success, ARNN architectures often operate as black boxes without a clear connection to underlying physic ...

Nature Portfolio2023

A Neural-Network-Based Convex Regularizer for Inverse Problems

Michaël Unser, Alexis Marie Frederic Goujon, Pakshal Narendra Bohra, Sebastian Jonas Neumayer, Stanislas Ducotterd

The emergence of deep-learning-based methods to solve image-reconstruction problems has enabled a significant increase in quality. Unfortunately, these new methods often lack reliability and explainability, and there is a growing interest to address these ...

2023

From Kernel Methods to Neural Networks: A Unifying Variational Formulation

Michaël Unser

The minimization of a data-fidelity term and an additive regularization functional gives rise to a powerful framework for supervised learning. In this paper, we present a unifying regularization functional that depends on an operator L\documentclass[12pt]{ ...

Springer2023

New dwarf galaxy candidates in the sphere of influence of the Sombrero galaxy

Oliver Müller

We report the discovery of 40 new satellite dwarf galaxy candidates in the sphere of influence of the Sombrero Galaxy (M104), the most luminous galaxy in the Local Volume. Using the Subaru Hyper Suprime-Cam, we surveyed 14.4 deg(2) of its surroundings, ext ...

Oxford Univ Press2023

Deep Generative Models for Autonomous Driving: from Motion Forecasting to Realistic Image Synthesis

Saeed Saadatnejad

Forecasting is a capability inherent in humans when navigating. Humans routinely plan their paths, considering the potential future movements of those around them. Similarly, to achieve comparable sophistication and safety, autonomous systems must embrace ...

EPFL2023

On combining denoising with learning-based image decoding

Touradj Ebrahimi, Michela Testolina

Noise is an intrinsic part of any sensor and is present, in various degrees, in any content that has been captured in real life environments. In imaging applications, several pre- and post-processing solutions have been proposed to cope with noise in captu ...

SPIE2022

BIGPrior: Towards Decoupling Learned Prior Hallucination and Data Fidelity in Image Restoration

Sabine Süsstrunk, Majed El Helou

Classic image-restoration algorithms use a variety of priors, either implicitly or explicitly. Their priors are hand-designed and their corresponding weights are heuristically assigned. Hence, deep learning methods often produce superior image restoration ...

2022

Resolution-robust Large Mask In painting with Fourier Convolutions

Anastasia Remizova

Modern image inpainting systems, despite the significant progress, often struggle with large missing areas, complex geometric structures, and high-resolution images. We find that one of the main reasons for that is the lack of an effective receptive field ...

IEEE COMPUTER SOC2022

Image Denoising with Control over Deep Network Hallucination

Sabine Süsstrunk, Majed El Helou

Deep image denoisers achieve state-of-the-art results but with a hidden cost. As witnessed in recent literature, these deep networks are capable of overfitting their training distributions, causing inaccurate hallucinations to be added to the output and ge ...

Society for Imaging Science and Technology (IS&T)2022

GALET: A deep learning image segmentation model for drone-based grain size analysis of gravel bars

Giovanni De Cesare, Christian Mörtl

In this study, we present the deep learning image segmentation model for drone-based grain size analysis of gravel bars called GALET. The objectives are to quantify the performance of the code and to test its applicability in river research and management. ...

International Association for Hydro-Environment Engineering and Research (IAHR)2022

A Long Time Ago in a Galaxy Far, Far Away: A Candidate z similar to 12 Galaxy in Early JWST CEERS Imaging

Michaela Hirschmann

We report the discovery of a candidate galaxy with a photo -z of z similar to 12 in the first epoch of the James Webb Space Telescope (JWST) Cosmic Evolution Early Release Science Survey. Following conservative selection criteria, we identify a source with ...

IOP Publishing Ltd2022

Self-supervised Segmentation via Background Inpainting

Pascal Fua, Mathieu Salzmann, Helge Jochen Rhodin, Isinsu Katircioglu, Victor Constantin

While supervised object detection and segmentation methods achieve impressive accuracy, they generalize poorly to images whose appearance significantly differs from the data they have been trained on. To address this when annotating data is prohibitively e ...

2022

PRIME: A Few Primitives Can Boost Robustness to Common Corruptions

Pascal Frossard, Seyed Mohsen Moosavi Dezfooli, Apostolos Modas, Guillermo Ortiz Jimenez

Despite their impressive performance on image classification tasks, deep networks have a hard time generalizing to unforeseen corruptions of their data. To fix this vulnerability, prior works have built complex data augmentation strategies, combining multi ...

SPRINGER INTERNATIONAL PUBLISHING AG2022

Latent Mechanisms of Polarization Switching from In Situ Electron Microscopy Observations

Vasiliki Tileli, Reinis Ignatans

In situ scanning transmission electron microscopy enables observation of the domain dynamics in ferroelectric materials as a function of externally applied bias and temperature. The resultant data sets contain a wealth of information on polarization switch ...

WILEY-V C H VERLAG GMBH2022

A Plug-and-Play Deep Image Prior

Volkan Cevher, Fabian Ricardo Latorre Gomez, Thomas Sanchez

Deep image priors (DIP) offer a novel approach for the regularization that leverages the inductive bias of a deep convolutional architecture in inverse problems. However, the quality of DIP approaches often degrades when the number of iterations exceeds a ...

IEEE2021

Deep Image Restoration: Between Data Fidelity and Learned Priors

Majed El Helou

Image restoration reconstructs, as faithfully as possible, an original image from a potentially degraded version of it. Image degradations can be of various types, for instance haze, unwanted reflections, optical or spectral aberrations, or other physicall ...

EPFL2021