iSeeBetter VS FUnIE-GAN

Compare iSeeBetter vs FUnIE-GAN and see what are their differences.

iSeeBetter

iSeeBetter: Spatio-Temporal Video Super Resolution using Recurrent-Generative Back-Projection Networks | Python3 | PyTorch | GANs | CNNs | ResNets | RNNs | Published in Springer Journal of Computational Visual Media, September 2020, Tsinghua University Press (by amanchadha)
Gans Gan Cnn resnet resnets Rnn Pytorch super-resolution srgan Psnr video-super-resolution Ssim self-supervised-learning unsupervised-learning generative-adversarial-network convolutional-neural-networks convolutional-neural-network recurrent-neural-networks residual-networks video-processing
Source Code
arxiv.org
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FUnIE-GAN

Fast underwater image enhancement for Improved Visual Perception. #TensorFlow #PyTorch #RAL2020 (by xahidbuffon)
image-enhancement ugan funie-gan underwater-images underwater-robotics visual-perception uiqm Ssim Psnr Measure
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iSeeBetter FUnIE-GAN
3 1
348 414
- -
0.0 1.8
almost 2 years ago over 3 years ago
C++ Python
MIT License GNU General Public License v3.0 or later
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
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iSeeBetter

Posts with mentions or reviews of iSeeBetter. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2022-07-27.

FUnIE-GAN

Posts with mentions or reviews of FUnIE-GAN. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2021-04-15.
  • Comparison of detection (with CNN) quality with color restoration of underwater images and without
    2 projects | /r/MLQuestions | 15 Apr 2021
    My diploma's theme has a bit specific application area - sunken technical objects. I have to restorate underwater images. And to prove useability of restoration method for autonomous underwater vechile (AUV) I decided to compare quality of CNN model train with preprocessing and without it. Due to the lack of data I use mirroring by way of augmentation. And to preprocess hundreds of images of images I used this method https://github.com/xahidbuffon/FUnIE-GAN .

What are some alternatives?

When comparing iSeeBetter and FUnIE-GAN you can also consider the following projects:

TecoGAN - This repo contains source code and materials for the TEmporally COherent GAN SIGGRAPH project.

prosr - Repository containing an independent implementation of the paper: "A Fully Progressive Approach to Single-Image Super-Resolution"

Fast-SRGAN - A Fast Deep Learning Model to Upsample Low Resolution Videos to High Resolution at 30fps

sunken-object - Compare quality of binary classification (detection) between natural underwater images and images with restoration

pytorch-gans - PyTorch implementation of GANs (Generative Adversarial Networks). DCGAN, Pix2Pix, CycleGAN, SRGAN

video-quality-metrics - Test specified presets/CRF values for the x264 or x265 encoder. Compares VMAF/SSIM/PSNR numerically & via graphs.

a-PyTorch-Tutorial-to-Super-Resolution - Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network | a PyTorch Tutorial to Super-Resolution

Seamoth - A python library for the Mate ROV competition by the MHS

ACNetGLSL - ACNet (Anime4KCPP Net) re-implemented in GLSL for real-time anime upscaling

piqa - PyTorch Image Quality Assessement package

libopenshot - OpenShot Video Library (libopenshot) is a free, open-source project dedicated to delivering high quality video editing, animation, and playback solutions to the world. API currently supports C++, Python, and Ruby.

Few-Shot-Patch-Based-Training - The official implementation of our SIGGRAPH 2020 paper Interactive Video Stylization Using Few-Shot Patch-Based Training

iSeeBetter vs TecoGAN FUnIE-GAN vs prosr iSeeBetter vs Fast-SRGAN FUnIE-GAN vs sunken-object iSeeBetter vs pytorch-gans FUnIE-GAN vs video-quality-metrics iSeeBetter vs a-PyTorch-Tutorial-to-Super-Resolution FUnIE-GAN vs Seamoth iSeeBetter vs ACNetGLSL FUnIE-GAN vs piqa iSeeBetter vs libopenshot iSeeBetter vs Few-Shot-Patch-Based-Training