209 papers with code • 1 benchmarks • 4 datasets

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Use these libraries to find SSIM models and implementations

Most implemented papers

The Unreasonable Effectiveness of Deep Features as a Perceptual Metric

richzhang/PerceptualSimilarity CVPR 2018

We systematically evaluate deep features across different architectures and tasks and compare them with classic metrics.

End-to-end Optimized Image Compression

tensorflow/models 5 Nov 2016

We describe an image compression method, consisting of a nonlinear analysis transformation, a uniform quantizer, and a nonlinear synthesis transformation.

Variational image compression with a scale hyperprior

tensorflow/compression ICLR 2018

We describe an end-to-end trainable model for image compression based on variational autoencoders.

A Fully Progressive Approach to Single-Image Super-Resolution

fperazzi/proSR 9 Apr 2018

Recent deep learning approaches to single image super-resolution have achieved impressive results in terms of traditional error measures and perceptual quality.

Towards Compact Single Image Super-Resolution via Contrastive Self-distillation

Booooooooooo/CSD 25 May 2021

Convolutional neural networks (CNNs) are highly successful for super-resolution (SR) but often require sophisticated architectures with heavy memory cost and computational overhead, significantly restricts their practical deployments on resource-limited devices.

TAC-GAN - Text Conditioned Auxiliary Classifier Generative Adversarial Network

dashayushman/TAC-GAN 19 Mar 2017

In this work, we present the Text Conditioned Auxiliary Classifier Generative Adversarial Network, (TAC-GAN) a text to image Generative Adversarial Network (GAN) for synthesizing images from their text descriptions.

DVC: An End-to-end Deep Video Compression Framework

GuoLusjtu/DVC CVPR 2019

Conventional video compression approaches use the predictive coding architecture and encode the corresponding motion information and residual information.

Progressive Image Deraining Networks: A Better and Simpler Baseline

csdwren/PReNet CVPR 2019

To handle this issue, this paper provides a better and simpler baseline deraining network by considering network architecture, input and output, and loss functions.

ReconResNet: Regularised Residual Learning for MR Image Reconstruction of Undersampled Cartesian and Radial Data

soumickmj/MRUnder 16 Mar 2021

It has been shown that the proposed framework can successfully reconstruct even for an acceleration factor of 20 for Cartesian (0. 968$\pm$0. 005) and 17 for radially (0. 962$\pm$0. 012) sampled data.

Learned Primal-dual Reconstruction

odlgroup/odl 20 Jul 2017

We propose the Learned Primal-Dual algorithm for tomographic reconstruction.