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
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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
Text in the Dark: Extremely Low-Light Text Image Enhancement
We also labeled texts in the extremely low-light See In the Dark (SID) and ordinary LOw-Light (LOL) datasets to allow for objective assessment of extremely low-light image enhancement through scene text tasks.
UVEB: A Large-scale Benchmark and Baseline Towards Real-World Underwater Video Enhancement
Learning-based underwater image enhancement (UIE) methods have made great progress.
On-board classification of underwater images using hybrid classical-quantum CNN based method
In the current work, we use quantum-classical hybrid machine learning methods for real-time under-water object recognition on-board an AUV for the first time.
Improving the perception of visual fiducial markers in the field using Adaptive Active Exposure Control
Accurate localization is fundamental for autonomous underwater vehicles (AUVs) to carry out precise tasks, such as manipulation and construction.
Real-world Instance-specific Image Goal Navigation for Service Robots: Bridging the Domain Gap with Contrastive Learning
To address this, we propose a novel method called Few-shot Cross-quality Instance-aware Adaptation (CrossIA), which employs contrastive learning with an instance classifier to align features between massive low- and few high-quality images.
BG-YOLO: A Bidirectional-Guided Method for Underwater Object Detection
When training the enhancement branch, the object detection subnet in the enhancement branch guides the image enhancement subnet to be optimized towards the direction that is most conducive to the detection task.
Seeing Text in the Dark: Algorithm and Benchmark
Localizing text in low-light environments is challenging due to visual degradations.
Separated Attention: An Improved Cycle GAN Based Under Water Image Enhancement Method
In this paper we have present an improved Cycle GAN based model for under water image enhancement.
Comparative Analysis of Image Enhancement Techniques for Brain Tumor Segmentation: Contrast, Histogram, and Hybrid Approaches
This study systematically investigates the impact of image enhancement techniques on Convolutional Neural Network (CNN)-based Brain Tumor Segmentation, focusing on Histogram Equalization (HE), Contrast Limited Adaptive Histogram Equalization (CLAHE), and their hybrid variations.
CodeEnhance: A Codebook-Driven Approach for Low-Light Image Enhancement
Low-light image enhancement (LLIE) aims to improve low-illumination images.
