Image Enhancement
139 papers with code • 2 benchmarks • 9 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 implementations
UIEB
ExDark
MIT-Adobe FiveK
PolyU Dataset
LLVIP
CUHK Image Cropping
PolarRR
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.
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?
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.
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
Powered by these two designs, Uformer enjoys a high capability for capturing both local and global dependencies for image restoration.
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.
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.
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.
