Advanced Image Reconstruction using Autoencoders is a project that explores the application of convolutional autoencoder models to enhance image quality. It employs sophisticated algorithms to intelligently reconstruct images, aiming to improve clarity and retain important details.

Jan 10, 2024 · This paper presents a fully automated pipeline using a sparse convolutional autoencoder for quality control (QC) of affine registrations in large-scale T1-weighted (T1w) and T2-weighted (T2w) magnetic resonance imaging (MRI) studies.

Train VGG-like and ResNet-like auto-encoder on image dataset like ImageNet. |──figs # result images. |── *.jpg. |──models. |──builder.py # build autoencoder models. |──resnet.py # resnet-like autoencoder. |──vgg.py # vgg-like autoencoder. |──run. |──eval.sh # command to evaluate single checkpoint.

Oct 3, 2024 · Explore how the integration of an improved autoencoder and a multi-stage training process enhances the image quality in SDXL.

Nov 27, 2023 · Autoencoders are type of a deep learning algorithm that performs encoding of an input to a compressed representation and decoding of the compressed representation to the same or different...

Flickr-Faces-HQ (FFHQ) is a high-quality image dataset of human faces, originally created as a benchmark for generative adversarial networks (GAN): To use pretrained autoencoder model, refer to "Load Pretrained Model and Run Inference" section in Notebook file"

Apr 30, 2025 · Based on this insight, we propose a new DAE training method that improves the quality of reconstructed images. We divide training into two phases. In the first phase, the DAE is trained as a vanilla autoencoder by always setting the noise level to the highest, forcing the encoder and decoder to populate the latent code with structural information.

To achieve quality control in image compression network, in this paper, we propose a quality controllable image compression network, Quality Controllable Variational Autoencoder (QCVAE). QC-VAE consists of the Quality-Feature-Level (QFL) model we proposed and the Hyperprior Continuously Variable Rate (HCVR) image compression network which can ...

Nov 3, 2023 · Autoencoders are a type of neural network that can be used to improve image quality, inpaint images, and detect anomalous images. They are being used in a variety of applications, such as medical imaging, remote sensing, and security.

Jan 10, 2024 · This paper presents a fully automated pipeline using a sparse convolutional autoencoder for quality control (QC) of affine registrations in large-scale T1-weighted (T1w) and T2-weighted (T2w) magnetic resonance imaging (MRI) studies.