Learning parametric functions for color image enhancement

In this work we propose a novel CNN-based method for image enhancement that simulates an expert retoucher. The method is fast and accurate at the same time thanks to the decoupling between the inference of the parameters and the color transformation. Specifically, the parameters are inferred from a downsampled version of the raw input image and the transformation is applied to the full resolution input. Different variants of the proposed enhancement method can be generated by varying the parametric functions used as color transformations (i.e. polynomial, piecewise, cosine and radial), and by varying how they are applied (i.e. channelwise or full color). Experimental results show that several variants of the proposed method outperform the state of the art on the MIT-Adobe FiveK dataset.