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
Most implemented papers
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.
Zero-Reference Deep Curve Estimation for Low-Light Image Enhancement
Li-Chongyi/Zero-DCE
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CVPR 2020
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.
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?
LLNet: A Deep Autoencoder Approach to Natural Low-light Image Enhancement
In surveillance, monitoring and tactical reconnaissance, gathering the right visual information from a dynamic environment and accurately processing such data are essential ingredients to making informed decisions which determines the success of an operation.
Underwater Image Enhancement via Medium Transmission-Guided Multi-Color Space Embedding
As a result, our network can effectively improve the visual quality of underwater images by exploiting multiple color spaces embedding and the advantages of both physical model-based and learning-based methods.
Retinexformer: One-stage Retinex-based Transformer for Low-light Image Enhancement
caiyuanhao1998/retinexformer
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ICCV 2023
When enhancing low-light images, many deep learning algorithms are based on the Retinex theory.
Simultaneous Enhancement and Super-Resolution of Underwater Imagery for Improved Visual Perception
In this paper, we introduce and tackle the simultaneous enhancement and super-resolution (SESR) problem for underwater robot vision and provide an efficient solution for near real-time applications.
Small-Object Detection in Remote Sensing Images with End-to-End Edge-Enhanced GAN and Object Detector Network
Jakaria08/Filter_Enhance_Detect
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Inspired by the success of edge enhanced GAN (EEGAN) and ESRGAN, we apply a new edge-enhanced super-resolution GAN (EESRGAN) to improve the image quality of remote sensing images and use different detector networks in an end-to-end manner where detector loss is backpropagated into the EESRGAN to improve the detection performance.
Learning to Enhance Low-Light Image via Zero-Reference Deep Curve Estimation
Li-Chongyi/Zero-DCE_extension
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This 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.
Uformer: A General U-Shaped Transformer for Image Restoration
ZhendongWang6/Uformer
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CVPR 2022
Powered by these two designs, Uformer enjoys a high capability for capturing both local and global dependencies for image restoration.
