Image Enhancement
309 papers with code • 6 benchmarks • 16 datasets
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
These leaderboards are used to track progress in Image Enhancement
| Trend | Dataset | Best Model | Paper | Code | Compare |
|---|---|---|---|---|---|
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
Libraries
Use these libraries to find Image Enhancement models and implementationsDatasets
UIEB
LLVIP
ExDark
MIT-Adobe FiveK
PolyU
Exposure-Errors
CUHK Image Cropping
SICE-Mix
Subtasks
Most implemented papers
Clearing the Skies: A deep network architecture for single-image rain removal
jinnovation/rainy-image-dataset
•
•
We introduce a deep network architecture called DerainNet for removing rain streaks from an image.
LIME: Low-light Image Enhancement via Illumination Map Estimation
When one captures images in low-light conditions, the images often suffer from low visibility.
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.
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.
Deep Underwater Image Enhancement
In an underwater scene, wavelength-dependent light absorption and scattering degrade the visibility of images, causing low contrast and distorted color casts.
Lesion Focused Super-Resolution
GinZhu/RDST
•
•
Super-resolution (SR) for image enhancement has great importance in medical image applications.
XNet: A convolutional neural network (CNN) implementation for medical X-Ray image segmentation suitable for small datasets
X-Ray image enhancement, along with many other medical image processing applications, requires the segmentation of images into bone, soft tissue, and open beam regions.
EdgeFool: An Adversarial Image Enhancement Filter
This loss function accounts for both image detail enhancement and class misleading objectives.
Learning Multi-Scale Photo Exposure Correction
In contrast, our proposed method targets both over- and underexposure errors in photographs.
DeepLPF: Deep Local Parametric Filters for Image Enhancement
sjmoran/deep_local_parametric_filters
•
•
CVPR 2020
We introduce a deep neural network, dubbed Deep Local Parametric Filters (DeepLPF), which regresses the parameters of these spatially localized filters that are then automatically applied to enhance the image.
