CURL: Neural Curve Layers for Global Image Enhancement

29 Nov 2019  ·  Sean Moran, Steven McDonagh, Gregory Slabaugh ·

We present a novel approach to adjust global image properties such as colour, saturation, and luminance using human-interpretable image enhancement curves, inspired by the Photoshop curves tool. Our method, dubbed neural CURve Layers (CURL), is designed as a multi-colour space neural retouching block trained jointly in three different colour spaces (HSV, CIELab, RGB) guided by a novel multi-colour space loss. The curves are fully differentiable and are trained end-to-end for different computer vision problems including photo enhancement (RGB-to-RGB) and as part of the image signal processing pipeline for image formation (RAW-to-RGB). To demonstrate the effectiveness of CURL we combine this global image transformation block with a pixel-level (local) image multi-scale encoder-decoder backbone network. In an extensive experimental evaluation we show that CURL produces state-of-the-art image quality versus recently proposed deep learning approaches in both objective and perceptual metrics, setting new state-of-the-art performance on multiple public datasets. Our code is publicly available at: https://github.com/sjmoran/CURL.

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Datasets


Results from the Paper


Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Photo Retouching MIT-Adobe 5k DIFAR (MSCA, level 1) PSNR 24.2 # 2
SSIM 0.88 # 2
Image Enhancement MIT-Adobe 5k DIFAR (MSCA, level 1) LPIPS 0.108 # 1
PSNR 24.2 # 1
SSIM 0.88 # 1

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