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Image Enhancement On Mit Adobe 5K

Metrics

PSNR on proRGB
SSIM on proRGB

Results

Performance results of various models on this benchmark

Model Name
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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