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 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 and a color image, wherein the night-vision image and the color image depict an overlapping and/or substantially identical region of interest (303, 304); ∘ determining 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 from the luminance values (L NIR) of the night-vision images; - determining, 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 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 and the color image of at least some of the several pairs of training images are taken separately, wherein the night-vision image is taken at night or under dark conditions and the corresponding color image is taken at daytime using visible light. - determining 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 and a color image, wherein the night-vision image and the color image depict an overlapping and/or substantially identical region of interest (303, 304); ∘ determining 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 from the luminance values (L NIR) of the night-vision images; - determining, 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 and a color image, wherein the night-vision image and the color image depict an overlapping and/or substantially identical region of interest (303, 304); ∘ determining 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 from the luminance values (L NIR) of the night-vision images; - determining, 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 the luminance values (L NIR_In) of the first night-vision image (NIR In) with the predicted chrominance values (C VIS_Rec);