A method for generating a first color image from a first night-vision image (NIRin), the first color image being in a color space which distinguishes between luminance and chrominance values, the method comprising - determining (S41) a prediction function (f Pred ) which maps luminance values of night-vision images to predicted chrominance values (C VIS_Rec ), wherein the predicted chrominance values (C VIS_Rec ) are similar to chrominance values of color images taken at day time corresponding to the night-vision images by ∘ obtaining several pairs of training images, each pair comprising a night-vision image (NIR) and a color image (VIS), wherein the night-vision image (NIR) and the color image (VIS) depict an overlapping and/or substantially identical region of interest ; ∘ determining (S41) the prediction function (f Pred ) by training a machine learning model on the training images to predict the chrominance values (C VIS ) of the color images (VIS) from the luminance values (L NIR ) of the night-vision images (NIR); - determining (S403), by using the prediction function (f Pred ), predicted chrominance values (C VIS_Rec ) for the first color image based on luminance values (L NIR_In ) of the first night-vision image (NIRin); - generating the first color image by combining (S404) the luminance values (L NIR_In ) of the first night-vision image (NIR In ) with the predicted chrominance values (C VIS_Rec ); wherein the machine learning method used for determining the prediction function (f Pred ) is based on neural networks; and wherein the night-vision image (NIR) and the color image (VIS) of at least some of the several pairs of training images are taken separately, wherein the night-vision image (NIR) is taken at night or under dark conditions and the corresponding color image (VIS) is taken at daytime using visible light. - determining (S41) a prediction function (f Pred ) which maps luminance values of night-vision images to predicted chrominance values (C VIS_Rec ), wherein the predicted chrominance values (C VIS_Rec ) are similar to chrominance values of color images taken at day time corresponding to the night-vision images by ∘ obtaining several pairs of training images, each pair comprising a night-vision image (NIR) and a color image (VIS), wherein the night-vision image (NIR) and the color image (VIS) depict an overlapping and/or substantially identical region of interest ; ∘ determining (S41) the prediction function (f Pred ) by training a machine learning model on the training images to predict the chrominance values (C VIS ) of the color images (VIS) from the luminance values (L NIR ) of the night-vision images (NIR); - determining (S403), by using the prediction function (f Pred ), predicted chrominance values (C VIS_Rec ) for the first color image based on luminance values (L NIR_In ) of the first night-vision image (NIRin); ∘ obtaining several pairs of training images, each pair comprising a night-vision image (NIR) and a color image (VIS), wherein the night-vision image (NIR) and the color image (VIS) depict an overlapping and/or substantially identical region of interest ; ∘ determining (S41) the prediction function (f Pred ) by training a machine learning model on the training images to predict the chrominance values (C VIS ) of the color images (VIS) from the luminance values (L NIR ) of the night-vision images (NIR); - determining (S403), by using the prediction function (f Pred ), predicted chrominance values (C VIS_Rec ) for the first color image based on luminance values (L NIR_In ) of the first night-vision image (NIRin); - generating the first color image by combining (S404) the luminance values (L NIR_In ) of the first night-vision image (NIR In ) with the predicted chrominance values (C VIS_Rec ); <EOS>