Computer-implemented method for normalizing images from a predetermined type of image acquisition device using a machine learning unit, the method comprising the steps of: - Receiving a set of image data with images (I1 to I9) at a decomposition unit, wherein the image data have been generated by being converted from detector signals, acquired at a detector of an image acquisition device, wherein the detector signals are converted by means of applying different settings in image acquisition device-specific processing algorithms; - At a decomposition unit: Decomposing each of the images (I1 to I9) of the set of images into components (C1 to Cn) by incorporating at least information from the different settings of the image acquisition device-specific processing algorithms; - At a normalizing unit: Normalizing each of said components (C1 to Cn) by means of a machine learning unit by processing at least information from the different settings of the image acquisition device-specific processing algorithms to provide a set of normalized images (CN1 to CNn) with a decreased variability score, wherein the method further comprises: - Receiving said normalized components (CN1 to CNn) at an input interface of a second machine learning unit and executing a second machine learning algorithm with the normalized components (CN1 to CNn) for a pre-defined task, wherein said normalized components (CN1 to CNn) are entered as feature maps of a convolutional layer of a neural network, - during training of the machine learning unit used for normalizing, at least a part of the parameters are jointly trained with the parameters, which are used and processed in the second machine learning unit by using the same cost function. - Receiving a set of image data with images (I1 to I9) at a decomposition unit, wherein the image data have been generated by being converted from detector signals, acquired at a detector of an image acquisition device, wherein the detector signals are converted by means of applying different settings in image acquisition device-specific processing algorithms; - At a decomposition unit: Decomposing each of the images (I1 to I9) of the set of images into components (C1 to Cn) by incorporating at least information from the different settings of the image acquisition device-specific processing algorithms; - At a normalizing unit: Normalizing each of said components (C1 to Cn) by means of a machine learning unit by processing at least information from the different settings of the image acquisition device-specific processing algorithms to provide a set of normalized images (CN1 to CNn) with a decreased variability score, - Receiving said normalized components (CN1 to CNn) at an input interface of a second machine learning unit and executing a second machine learning algorithm with the normalized components (CN1 to CNn) for a pre-defined task, wherein said normalized components (CN1 to CNn) are entered as feature maps of a convolutional layer of a neural network, - during training of the machine learning unit used for normalizing, at least a part of the parameters are jointly trained with the parameters, which are used and processed in the second machine learning unit by using the same cost function.