About
Benchmarks
| TREND | DATASET | BEST METHOD | PAPER TITLE | PAPER | CODE | COMPARE |
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Subtasks
Greatest papers with code
Learning to See in the Dark
CVPR 2018
• cchen156/Learning-to-See-in-the-Dark
• 
Imaging in low light is challenging due to low photon count and low SNR.
DeblurGAN: Blind Motion Deblurring Using Conditional Adversarial Networks
CVPR 2018
• KupynOrest/DeblurGAN
• 
The quality of the deblurring model is also evaluated in a novel way on a real-world problem -- object detection on (de-)blurred images.
Ranked #2 on
Deblurring
on REDS
EDVR: Video Restoration with Enhanced Deformable Convolutional Networks
• open-mmlab/mmsr
•
In this work, we propose a novel Video Restoration framework with Enhanced Deformable networks, termed EDVR, to address these challenges.
Ranked #1 on
Deblurring
on REDS
Deep Plug-and-Play Super-Resolution for Arbitrary Blur Kernels
In this paper, we propose a principled formulation and framework by extending bicubic degradation based deep SISR with the help of plug-and-play framework to handle LR images with arbitrary blur kernels.
GIMP-ML: Python Plugins for using Computer Vision Models in GIMP
Apart from these, several image manipulation techniques using these plugins have been compiled and demonstrated in the YouTube channel (https://youtube. com/user/kritiksoman) with the objective of demonstrating the use-cases for machine learning based image modification.
COLORIZATION DEBLURRING DENOISING IMAGE INPAINTING IMAGE MANIPULATION IMAGE SUPER-RESOLUTION MONOCULAR DEPTH ESTIMATION SEMANTIC SEGMENTATION SINGLE IMAGE DEHAZING VIDEO FRAME INTERPOLATION
DeblurGAN-v2: Deblurring (Orders-of-Magnitude) Faster and Better
ICCV 2019
• kritiksoman/GIMP-ML
•
We present a new end-to-end generative adversarial network (GAN) for single image motion deblurring, named DeblurGAN-v2, which considerably boosts state-of-the-art deblurring efficiency, quality, and flexibility.
Ranked #14 on
Deblurring
on GoPro
(using extra training data)
Learning Deep CNN Denoiser Prior for Image Restoration
CVPR 2017
• cszn/ircnn
•
Recent works have revealed that, with the aid of variable splitting techniques, denoiser prior can be plugged in as a modular part of model-based optimization methods to solve other inverse problems (e. g., deblurring).
Ranked #1 on
Color Image Denoising
on BSD68 sigma5
COLOR IMAGE DENOISING DEBLURRING IMAGE DENOISING IMAGE RESTORATION
Residual Dense Network for Image Restoration
• yulunzhang/RDN
•
We fully exploit the hierarchical features from all the convolutional layers.
Ranked #1 on
Color Image Denoising
on Kodak24 sigma30
DEBLURRING IMAGE COMPRESSION IMAGE COMPRESSION ARTIFACT REDUCTION IMAGE DENOISING IMAGE RESTORATION IMAGE SUPER-RESOLUTION
Neural Blind Deconvolution Using Deep Priors
CVPR 2020
• csdwren/SelfDeblur
•
To connect MAP and deep models, we in this paper present two generative networks for respectively modeling the deep priors of clean image and blur kernel, and propose an unconstrained neural optimization solution to blind deconvolution.
Deep Video Deblurring for Hand-Held Cameras
We show that the features learned from this dataset extend to deblurring motion blur that arises due to camera shake in a wide range of videos, and compare the quality of results to a number of other baselines.