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
UHDM
Subtasks
Latest papers
NTIRE 2024 Challenge on Low Light Image Enhancement: Methods and Results
This paper reviews the NTIRE 2024 low light image enhancement challenge, highlighting the proposed solutions and results.
CRNet: A Detail-Preserving Network for Unified Image Restoration and Enhancement Task
In real-world scenarios, images captured often suffer from blurring, noise, and other forms of image degradation, and due to sensor limitations, people usually can only obtain low dynamic range images.
MambaUIE&SR: Unraveling the Ocean's Secrets with Only 2.8 FLOPs
In addition, combining CNN and Transformer can effectively combine global and local information for enhancement.
Equipping Diffusion Models with Differentiable Spatial Entropy for Low-Light Image Enhancement
shermanlian/spatial-entropy-loss
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In this work, we propose a novel method that shifts the focus from a deterministic pixel-by-pixel comparison to a statistical perspective, emphasizing the learning of distributions rather than individual pixel values.
Low-Light Image Enhancement Framework for Improved Object Detection in Fisheye Lens Datasets
daitranskku/aic2024-track4-team15
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This study addresses the evolving challenges in urban traffic monitoring detection systems based on fisheye lens cameras by proposing a framework that improves the efficacy and accuracy of these systems.
Taming Lookup Tables for Efficient Image Retouching
Existing enhancement models often optimize for high performance while falling short of reducing hardware inference time and power consumption, especially on edge devices with constrained computing and storage resources.
Burst Super-Resolution with Diffusion Models for Improving Perceptual Quality
placerkyo/bsrd
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In our proposed method, on the other hand, burst LR features are used to reconstruct the initial burst SR image that is fed into an intermediate step in the diffusion model.
Residual Dense Swin Transformer for Continuous Depth-Independent Ultrasound Imaging
Ultrasound imaging is crucial for evaluating organ morphology and function, yet depth adjustment can degrade image quality and field-of-view, presenting a depth-dependent dilemma.
AdaIR: Adaptive All-in-One Image Restoration via Frequency Mining and Modulation
c-yn/adair
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Our approach is motivated by the observation that different degradation types impact the image content on different frequency subbands, thereby requiring different treatments for each restoration task.
End-To-End Underwater Video Enhancement: Dataset and Model
ddz16/UVENet
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To fill this gap, we construct the Synthetic Underwater Video Enhancement (SUVE) dataset, comprising 840 diverse underwater-style videos paired with ground-truth reference videos.
