The grid method is a technique suitable for the measurement of in‐plane displacement and
strain components on specimens undergoing a small deformation. It relies on a regular …

Abstract Neural Radiance Fields (NeRF) is a technique for high quality novel view synthesis
from a collection of posed input images. Like most view synthesis methods, NeRF uses …

We introduce a set of image transformations that can be used as corruptions to evaluate the
robustness of models as well as data augmentation mechanisms for training neural …

In this paper, we introduce NBNet, a novel framework for image denoising. Unlike previous
works, we propose to tackle this challenging problem from a new perspective: noise …

Previous raw image-based low-light image enhancement methods predominantly relied on
feed-forward neural networks to learn deterministic map**s from low-light to normally …

While deep convolutional neural networks (CNNs) have achieved impressive success in
image denoising with additive white Gaussian noise (AWGN), their performance remains …

Blind-spot network (BSN) and its variants have made significant advances in self-supervised
denoising. Nevertheless, they are still bound to synthetic noisy inputs due to less practical …

The availability of large-scale datasets has helped unleash the true potential of deep
convolutional neural networks (CNNs). However, for the single-image denoising problem …

The last decade has seen an astronomical shift from imaging with DSLR and point-and-
shoot cameras to imaging with smartphone cameras. Due to the small aperture and sensor …

Abstract Machine learning techniques work best when the data used for training resembles
the data used for evaluation. This holds true for learned single-image denoising algorithms …