Benchmarks
| TREND | DATASET | BEST METHOD | PAPER TITLE | PAPER | CODE | COMPARE |
|---|---|---|---|---|---|---|
|
Greatest papers with code
End-to-end Optimized Image Compression
We describe an image compression method, consisting of a nonlinear analysis transformation, a uniform quantizer, and a nonlinear synthesis transformation.
The Unreasonable Effectiveness of Deep Features as a Perceptual Metric
CVPR 2018
• richzhang/PerceptualSimilarity
• 
We systematically evaluate deep features across different architectures and tasks and compare them with classic metrics.
A Fully Progressive Approach to Single-Image Super-Resolution
• fperazzi/proSR
•
Recent deep learning approaches to single image super-resolution have achieved impressive results in terms of traditional error measures and perceptual quality.
Ranked #8 on
Image Super-Resolution
on Set14 - 4x upscaling
BASNet: Boundary-Aware Salient Object Detection
In this paper, we propose a predict-refine architecture, BASNet, and a new hybrid loss for Boundary-Aware Salient object detection.
Ranked #1 on
Salient Object Detection
on DUTS-TE
CAMOUFLAGED OBJECT SEGMENTATION RGB SALIENT OBJECT DETECTION SALIENCY PREDICTION SALIENT OBJECT DETECTION SSIM
AOD-Net: All-In-One Dehazing Network
ICCV 2017
• kritiksoman/GIMP-ML
•
This paper proposes an image dehazing model built with a convolutional neural network (CNN), called All-in-One Dehazing Network (AOD-Net).
State-of-the-Art Machine Learning MRI Reconstruction in 2020: Results of the Second fastMRI Challenge
Accelerating MRI scans is one of the principal outstanding problems in the MRI research community.
Structure-Preserving Super Resolution with Gradient Guidance
In this paper, we propose a structure-preserving super resolution method to alleviate the above issue while maintaining the merits of GAN-based methods to generate perceptual-pleasant details.
Ranked #34 on
Image Super-Resolution
on Urban100 - 4x upscaling
Learning Image-adaptive 3D Lookup Tables for High Performance Photo Enhancement in Real-time
• HuiZeng/Image-Adaptive-3DLUT
•
The small CNN works on the down-sampled version of the input image to predict content-dependent weights to fuse the multiple basis 3D LUTs into an image-adaptive one, which is employed to transform the color and tone of source images efficiently.
Single Image Reflection Separation with Perceptual Losses
CVPR 2018
• ceciliavision/perceptual-reflection-removal
• 
Our loss function includes two perceptual losses: a feature loss from a visual perception network, and an adversarial loss that encodes characteristics of images in the transmission layers.
RANSAC-Flow: generic two-stage image alignment
ECCV 2020
• XiSHEN0220/RANSAC-Flow
•
Coarse alignment is performed using RANSAC on off-the-shelf deep features.