Search Results for author:
Found 10 papers, 2 papers with code
Text-guided Explorable Image Super-resolution
In this paper, we introduce the problem of zero-shot text-guided exploration of the solutions to open-domain image super-resolution.
Robustness and Exploration of Variational and Machine Learning Approaches to Inverse Problems: An Overview
This paper attempts to provide an overview of current approaches for solving inverse problems in imaging using variational methods and machine learning.
Evaluating Adversarial Robustness of Low dose CT Recovery
Both classical approaches and deep networks are affected by such attacks leading to changes in the visual appearance of localized lesions, for extremely small perturbations.
On the unreasonable vulnerability of transformers for image restoration -- and an easy fix
We consider the recently proposed Restormer model, as well as NAFNet and the "Baseline network" which are both simplified versions of a Restormer.
LDEdit: Towards Generalized Text Guided Image Manipulation via Latent Diffusion Models
Our approach exploits recent Latent Diffusion Models (LDM) for text to image generation to achieve zero-shot text guided manipulation.
On Adversarial Robustness of Deep Image Deblurring
Recent approaches employ deep learning-based solutions for the recovery of a sharp image from its blurry observation.
A Simple Strategy to Provable Invariance via Orbit Mapping
Many applications require robustness, or ideally invariance, of neural networks to certain transformations of input data.
Training or Architecture? How to Incorporate Invariance in Neural Networks
Many applications require the robustness, or ideally the invariance, of a neural network to certain transformations of input data.
A Simple Domain Shifting Networkfor Generating Low Quality Images
Deep Learning systems have proven to be extremely successful for image recognition tasks for which significant amounts of training data is available, e. g., on the famous ImageNet dataset.
A Generative Model for Generic Light Field Reconstruction
We develop a generative model conditioned on the central view of the light field and incorporate this as a prior in an energy minimization framework to address diverse light field reconstruction tasks.
