# SSIM

209 papers with code • 1 benchmarks • 4 datasets

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## Libraries

Use these libraries to find SSIM models and implementations
4 papers
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4 papers
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3 papers
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2 papers
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# The Unreasonable Effectiveness of Deep Features as a Perceptual Metric

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

25

# End-to-end Optimized Image Compression

5 Nov 2016

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

13

# Variational image compression with a scale hyperprior

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

13

# A Fully Progressive Approach to Single-Image Super-Resolution

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.

6

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

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.

6

# TAC-GAN - Text Conditioned Auxiliary Classifier Generative Adversarial Network

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.

4

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

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

4

# Progressive Image Deraining Networks: A Better and Simpler Baseline

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

4

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

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.

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# Learned Primal-dual Reconstruction

20 Jul 2017

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

3