Image Enhancement
306 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
Latest papers with no code
Physics-Inspired Synthesized Underwater Image Dataset
This paper introduces the physics-inspired synthesized underwater image dataset (PHISWID), a dataset tailored for enhancing underwater image processing through physics-inspired image synthesis.
DI-Retinex: Digital-Imaging Retinex Theory for Low-Light Image Enhancement
Many existing methods for low-light image enhancement (LLIE) based on Retinex theory ignore important factors that affect the validity of this theory in digital imaging, such as noise, quantization error, non-linearity, and dynamic range overflow.
RAVE: Residual Vector Embedding for CLIP-Guided Backlit Image Enhancement
Instead, based on CLIP embeddings of backlit and well-lit images from training data, we compute the residual vector in the embedding space as a simple difference between the mean embeddings of the well-lit and backlit images.
Specularity Factorization for Low-Light Enhancement
We present a new additive image factorization technique that treats images to be composed of multiple latent specular components which can be simply estimated recursively by modulating the sparsity during decomposition.
CLIPtone: Unsupervised Learning for Text-based Image Tone Adjustment
Recent image tone adjustment (or enhancement) approaches have predominantly adopted supervised learning for learning human-centric perceptual assessment.
Towards Robust Event-guided Low-Light Image Enhancement: A Large-Scale Real-World Event-Image Dataset and Novel Approach
To this end, we propose a real-world (indoor and outdoor) dataset comprising over 30K pairs of images and events under both low and normal illumination conditions.
A Real-Time Framework for Domain-Adaptive Underwater Object Detection with Image Enhancement
Furthermore, to ensure balanced training for both tasks, we present a multi-stage training strategy aimed at consistently enhancing their performance.
Test-time Adaptation Meets Image Enhancement: Improving Accuracy via Uncertainty-aware Logit Switching
We hypothesize that enhancing the input image reduces prediction's uncertainty and increase the accuracy of TTA methods.
PQDynamicISP: Dynamically Controlled Image Signal Processor for Any Image Sensors Pursuing Perceptual Quality
Full DNN-based image signal processors (ISPs) have been actively studied and have achieved superior image quality compared to conventional ISPs.
Multiple Latent Space Mapping for Compressed Dark Image Enhancement
Since texture details intertwine with compression artifacts in compressed dark images, detail enhancement and blocking artifacts suppression contradict each other in image space.
