Color Enhancement Using Global Parameters and Local Features Learning

This paper proposes a neural network to learn global parameters and extract local features for color enhancement. Firstly, the global parameters extractor subnetwork with dilated convolution is used to estimate a global color transformation matrix. The introduction of the dilated convolution enhances the ability to aggregate spatial information. Secondly, the local features extractor subnetwork with a light dense block structure is designed to learn the matrix of local details. Finally, an enhancement map is obtained by multiplying these two matrices. A novel combination of loss functions is formulated to make the color of the generated image more consistent with that of the target. The enhanced image is formed by adding the original image with an enhancement map. Thus, we make it possible to adjust the enhancement intensity by multiplying the enhancement map with a weighting coefficient. We conduct experiments on the MIT-Adobe FiveK benchmark, and our algorithm generates superior performance compared with the state-of-the-art methods on images and videos, both qualitatively and quantitatively.