Laplace operator based multi-channel image filters learning

Abstract

Learning an image filter from a pair of images composed of an original image and its filtered version has attracted increasing attention in the image processing region. However, the existing methods only take the color information into consideration to learn an image filter, which cannot accurately depict the luminance and some style changes of images so that the generated learning results are inaccurate. This paper proposes a Laplace operator based multi-channel learning approach to learn a multi-channel filter from an original image and its filtered version. Different from previous method, we adopt a strategy to learn a multi-channel filter which can accurately reconstruct the filtered image. In addition, benefiting from the Laplace regularization based multi-channel learning model, we can make the neighbor pixels of each channel have the similar weights and learn more homogeneous weight maps for each channel filter, thereby generating more accurate results with less time. Due to the multi-channel representation of our learnt filter, we can edit the luminance style and the color style of images by changing the weights of different channel filter. Furthermore, we can edit the effect of the image filters by tuning the weights of some basis filters. At last, extensive experiments well validate the performance of our method over state-of-the-art methods in terms of accuracy and speed.