Image Enhancement
302 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
UHDM
Subtasks
Most implemented papers
Deep Burst Denoising
Ourshanabi/Burst-denoising
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ECCV 2018
One strategy for mitigating noise in a low-light situation is to increase the shutter time of the camera, thus allowing each photosite to integrate more light and decrease noise variance.
Single Image Reflection Separation with Perceptual Losses
ceciliavision/perceptual-reflection-removal
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CVPR 2018
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.
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.
Fast Underwater Image Enhancement for Improved Visual Perception
In this paper, we present a conditional generative adversarial network-based model for real-time underwater image enhancement.
Attention Guided Low-light Image Enhancement with a Large Scale Low-light Simulation Dataset
yu-li/AGLLNet
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Low-light image enhancement is challenging in that it needs to consider not only brightness recovery but also complex issues like color distortion and noise, which usually hide in the dark.
RIS-GAN: Explore Residual and Illumination with Generative Adversarial Networks for Shadow Removal
To our best knowledge, we are the first one to explore residual and illumination for shadow removal.
CURL: Neural Curve Layers for Global Image Enhancement
sjmoran/CURL
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We present a novel approach to adjust global image properties such as colour, saturation, and luminance using human-interpretable image enhancement curves, inspired by the Photoshop curves tool.
Low-Light Image and Video Enhancement Using Deep Learning: A Survey
Li-Chongyi/Lighting-the-Darkness-in-the-Deep-Learning-Era-Open
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Low-light image enhancement (LLIE) aims at improving the perception or interpretability of an image captured in an environment with poor illumination.
Structure-consistent Restoration Network for Cataract Fundus Image Enhancement
liamheng/arcnet-medical-image-enhancement
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In this paper, to circumvent the strict deployment requirement, a structure-consistent restoration network (SCR-Net) for cataract fundus images is developed from synthesized data that shares an identical structure.
Clearing the Skies: A deep network architecture for single-image rain removal
jinnovation/rainy-image-dataset
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We introduce a deep network architecture called DerainNet for removing rain streaks from an image.
