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Image Enhancement On Mit Adobe 5K

평가 지표

PSNR on proRGB
SSIM on proRGB

평가 결과

이 벤치마크에서 각 모델의 성능 결과

모델 이름
PSNR on proRGB
SSIM on proRGB
Paper TitleRepository
RSFNet-map25.490.924RSFNet: A White-Box Image Retouching Approach using Region-Specific Color Filters
SepLUT25.470.921SepLUT: Separable Image-adaptive Lookup Tables for Real-time Image Enhancement
AdaInt25.490.926AdaInt: Learning Adaptive Intervals for 3D Lookup Tables on Real-time Image Enhancement
4D LUT24.610.9184D LUT: Learnable Context-Aware 4D Lookup Table for Image Enhancement-
PQDynamicISP25.530.928PQDynamicISP: Dynamically Controlled Image Signal Processor for Any Image Sensors Pursuing Perceptual Quality-
DIFAR (MSCA, level 1)24.20.88CURL: Neural Curve Layers for Global Image Enhancement-
Retinexformer25.980.957Retinexformer: One-stage Retinex-based Transformer for Low-light Image Enhancement
DeepLPF23.930.903DeepLPF: Deep Local Parametric Filters for Image Enhancement
MTFE25.46-Multiple transformation function estimation for image enhancement
3D LUT25.210.922Learning Image-adaptive 3D Lookup Tables for High Performance Photo Enhancement in Real-time
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Image Enhancement On Mit Adobe 5K | SOTA | HyperAI초신경