Search Results for author:
Found 11 papers, 5 papers with code
NeuSD: Surface Completion with Multi-View Text-to-Image Diffusion
We present a novel method for 3D surface reconstruction from multiple images where only a part of the object of interest is captured.
Iterative Reweighted Least Squares Networks With Convergence Guarantees for Solving Inverse Imaging Problems
In this work we present a novel optimization strategy for image reconstruction tasks under analysis-based image regularization, which promotes sparse and/or low-rank solutions in some learned transform domain.
Learning Sparse and Low-Rank Priors for Image Recovery via Iterative Reweighted Least Squares Minimization
We introduce a novel optimization algorithm for image recovery under learned sparse and low-rank constraints, which we parameterize as weighted extensions of the $\ell_p^p$-vector and $\mathcal S_p^p$ Schatten-matrix quasi-norms for $0\!<p\!\le1$, respectively.
DeepRLS: A Recurrent Network Architecture with Least Squares Implicit Layers for Non-blind Image Deconvolution
In this work, we study the problem of non-blind image deconvolution and propose a novel recurrent network architecture that leads to very competitive restoration results of high image quality.
Microscopy Image Restoration with Deep Wiener-Kolmogorov filters
Microscopy is a powerful visualization tool in biology, enabling the study of cells, tissues, and the fundamental biological processes; yet, the observed images typically suffer from blur and background noise.
Iterative Residual CNNs for Burst Photography Applications
In this work, we focus on the fact that every frame of a burst sequence can be accurately described by a forward (physical) model.
Iterative Joint Image Demosaicking and Denoising using a Residual Denoising Network
Modern approaches try to jointly solve these problems, i. e. joint denoising-demosaicking which is an inherently ill-posed problem given that two-thirds of the intensity information is missing and the rest are perturbed by noise.
Deep Image Demosaicking using a Cascade of Convolutional Residual Denoising Networks
Demosaicking and denoising are among the most crucial steps of modern digital camera pipelines and their joint treatment is a highly ill-posed inverse problem where at-least two-thirds of the information are missing and the rest are corrupted by noise.
Universal Denoising Networks : A Novel CNN Architecture for Image Denoising
As opposed to most of the existing deep network approaches, which require the training of a specific model for each considered noise level, the proposed models are able to handle a wide range of noise levels using a single set of learned parameters, while they are very robust when the noise degrading the latent image does not match the statistics of the noise used during training.
Non-Local Color Image Denoising with Convolutional Neural Networks
We propose a novel deep network architecture for grayscale and color image denoising that is based on a non-local image model.
