About
Image Enhancement is basically improving the interpretability or perception of information in images for human viewers and providing ‘better’ input for other automated image processing techniques. The principal objective of Image Enhancement is to modify attributes of an image to make it more suitable for a given task and a specific observer.
Source: A Comprehensive Review of Image Enhancement Techniques
Benchmarks
| TREND | DATASET | BEST METHOD | PAPER TITLE | PAPER | CODE | COMPARE |
|---|---|---|---|---|---|---|
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Subtasks
Datasets
Greatest papers with code
EnlightenGAN: Deep Light Enhancement without Paired Supervision
Deep learning-based methods have achieved remarkable success in image restoration and enhancement, but are they still competitive when there is a lack of paired training data?
Deep Photo Enhancer: Unpaired Learning for Image Enhancement From Photographs With GANs
CVPR 2018
• nothinglo/Deep-Photo-Enhancer
• 
First, we augment the U-Net with global features and show that it is more effective.
Deep Bilateral Learning for Real-Time Image Enhancement
• google/hdrnet
• 
For this, we introduce a new neural network architecture inspired by bilateral grid processing and local affine color transforms.
Deep Retinex Decomposition for Low-Light Enhancement
Based on the decomposition, subsequent lightness enhancement is conducted on illumination by an enhancement network called Enhance-Net, and for joint denoising there is a denoising operation on reflectance.
Ranked #5 on
Low-Light Image Enhancement
on MEF
Getting to Know Low-light Images with The Exclusively Dark Dataset
Thus, we propose the Exclusively Dark dataset to elevate this data drought, consisting exclusively of ten different types of low-light images (i. e. low, ambient, object, single, weak, strong, screen, window, shadow and twilight) captured in visible light only with image and object level annotations.
Learning Enriched Features for Real Image Restoration and Enhancement
With the goal of recovering high-quality image content from its degraded version, image restoration enjoys numerous applications, such as in surveillance, computational photography, medical imaging, and remote sensing.
IMAGE DENOISING IMAGE ENHANCEMENT IMAGE RESTORATION SUPER-RESOLUTION
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.
Zero-Reference Deep Curve Estimation for Low-Light Image Enhancement
CVPR 2020
• Li-Chongyi/Zero-DCE
• 
The paper presents a novel method, Zero-Reference Deep Curve Estimation (Zero-DCE), which formulates light enhancement as a task of image-specific curve estimation with a deep network.
Ranked #1 on
Low-Light Image Enhancement
on NPE
An Experimental-based Review of Image Enhancement and Image Restoration Methods for Underwater Imaging
Underwater images play a key role in ocean exploration, but often suffer from severe quality degradation due to light absorption and scattering in water medium.
Blind Video Temporal Consistency via Deep Video Prior
NeurIPS 2020
• ChenyangLEI/deep-video-prior
•
Extensive quantitative and perceptual experiments show that our approach obtains superior performance than state-of-the-art methods on blind video temporal consistency.
COLORIZATION IMAGE DEHAZING IMAGE ENHANCEMENT OPTICAL FLOW ESTIMATION STYLE TRANSFER