SOTAVerified

Image Enhancement

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

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 101–125 of 983 papers

TitleStatusHype
Domain Adaptation for Underwater Image Enhancement via Content and Style SeparationCode1
For Overall Nighttime Visibility: Integrate Irregular Glow Removal With Glow-Aware EnhancementCode1
DPF-Net: Physical Imaging Model Embedded Data-Driven Underwater Image EnhancementCode1
GDB: Gated convolutions-based Document BinarizationCode1
A Generic Fundus Image Enhancement Network Boosted by Frequency Self-supervised Representation LearningCode1
Generalized Lightness Adaptation with Channel Selective NormalizationCode1
DNF: Decouple and Feedback Network for Seeing in the DarkCode1
Glow in the Dark: Low-Light Image Enhancement with External MemoryCode1
Harmonizing Pathological and Normal Pixels for Pseudo-healthy SynthesisCode1
High-Fidelity Document Stain Removal via A Large-Scale Real-World Dataset and A Memory-Augmented TransformerCode1
A CNN-Based Blind Denoising Method for Endoscopic ImagesCode1
Burst Denoising of Dark ImagesCode1
HUPE: Heuristic Underwater Perceptual Enhancement with Semantic Collaborative LearningCode1
DocEnTr: An End-to-End Document Image Enhancement TransformerCode1
Early Exit or Not: Resource-Efficient Blind Quality Enhancement for Compressed ImagesCode1
Illumination-Aware Image Quality Assessment for Enhanced Low-light ImageCode1
Dimma: Semi-supervised Low Light Image Enhancement with Adaptive DimmingCode1
Image Demoireing with Learnable Bandpass FiltersCode1
Diff-Mosaic: Augmenting Realistic Representations in Infrared Small Target Detection via Diffusion PriorCode1
DisC-Diff: Disentangled Conditional Diffusion Model for Multi-Contrast MRI Super-ResolutionCode1
Boosting Object Detection with Zero-Shot Day-Night Domain AdaptationCode1
Adversarial Color Enhancement: Generating Unrestricted Adversarial Images by Optimizing a Color FilterCode1
DGD-cGAN: A Dual Generator for Image Dewatering and RestorationCode1
Better Than Reference In Low Light Image Enhancement: Conditional Re-Enhancement NetworksCode1
DMFourLLIE: Dual-Stage and Multi-Branch Fourier Network for Low-Light Image EnhancementCode1
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All datasetsMIT-Adobe 5kTIP 2018Exposure-ErrorsMIT-Adobe FiveKSICE-GradSICE-Mix

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HG-MTFEPSNR on proRGB25.69—Unverified
2PQDynamicISPPSNR on proRGB25.53—Unverified
3AdaIntPSNR on proRGB25.49—Unverified
4RSFNet-mapPSNR on proRGB25.49—Unverified
5SepLUTPSNR on proRGB25.47—Unverified
6MTFEPSNR on proRGB25.46—Unverified
73D LUTPSNR on proRGB25.21—Unverified
8RetinexformerPSNR on sRGB24.94—Unverified
94D LUTPSNR on proRGB24.61—Unverified
10DIFAR (MSCA, level 1)PSNR on proRGB24.2—Unverified
#ModelMetricClaimedVerifiedStatus
1ESDNet-LPSNR30.11—Unverified
2MBCNNPSNR30.03—Unverified
3ESDNetPSNR29.81—Unverified
4Uformer-BPSNR29.28—Unverified
5MopNetPSNR27.75—Unverified
6DMCNNPSNR26.77—Unverified
#ModelMetricClaimedVerifiedStatus
1Exposure-slotPSNR23.18—Unverified
2CSECPSNR22.73—Unverified
3LCDPNetPSNR22.17—Unverified
4IATPSNR20.34—Unverified
5MSECPSNR20.21—Unverified
#ModelMetricClaimedVerifiedStatus
1TreEnhanceDeltaE11.25—Unverified
#ModelMetricClaimedVerifiedStatus
1CIDNetAverage PSNR13.45—Unverified
#ModelMetricClaimedVerifiedStatus
1CIDNetAverage PSNR13.43—Unverified