Paper Title: Interactive Reconstruction of Monte Carlo Image Sequences using a Recurrent Denoising Autoencoder Authors: Chakravarty R. Alla Chaitanya, Anton S. Kaplanyan, Christoph Schied, Marco Salvi ...

We investigate the impact of noise on time-reversal imaging and propose an approach that significantly enhances the detection of objects in noisy environments. Our method involves the decomposition of ...

Um denoising autoencoder é uma variação onde o modelo é treinado para reconstruir uma entrada limpa a partir de uma versão corrompida dessa mesma entrada. Isso os torna eficazes para tarefas de ...

In this paper, we propose a deep convolutional autoencoder combined with a variant of feature pyramid network for image denoising. We use simulated data in Blender software along with corrupted ...

Ideally, any suitable denoising autoencoder can be used, but we adopt the U-Net architecture (Ronneberger et al., 2015) originally developed for medical imaging applications and is robust for various ...

SeBiReNet (Nie et al., 2020) is a Siamese denoising autoencoder tested on feature disentanglement, pose denoising, and unsupervised cross-view HAR. According to Paoletti et al. (2021b), unsupervised ...