Recent works in image style transfer are overused… They usually synthesize undesirable results due to transferring exact colors to the wrong destination . In this work, we concentrate on learning low-level image transformation, especially color-shifting methods, then present a novel scheme to train color style transfer with ground-truth . We script Lightroom, a powerful tool in editing photos, to generate 600,000 training samples using 1,200 images from the Flick2K dataset and 500 user-generated presets with 69 settings . Experimental results show that our Deep Preset outperforms the previous works in color style transfers quantitatively and qualitatively. Our Deep Presets outperforms previous works . We also predict hyper-parameters (settings or preset) of the applied low-levels color transformation methods, and stylize content to have a similar color style as reference. We scripts Lightroom to generate Lightrooms to create 600,00 training samples. We script

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Keywords : color - style - transfer - works - deep -

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