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
Latest papers
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.
FogGuard: guarding YOLO against fog using perceptual loss
In this paper, we present a novel fog-aware object detection network called FogGuard, designed to address the challenges posed by foggy weather conditions.
7T MRI Synthesization from 3T Acquisitions
abbasilab/synthetic_7t_mri
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We demonstrate that the V-Net based model has superior performance in enhancing both single-site and multi-site MRI datasets compared to the existing benchmark model.
Misalignment-Robust Frequency Distribution Loss for Image Transformation
eezkni/fdl
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This paper aims to address a common challenge in deep learning-based image transformation methods, such as image enhancement and super-resolution, which heavily rely on precisely aligned paired datasets with pixel-level alignments.
