A deep learning based CT image noise reducing method, comprising: extracting test information from an input CT image; selecting at least one deep learning model corresponding to the test information, among a plurality of previously trained deep learning models; and outputting a CT image with a reduced noise with respect to the input CT image with the input CT image as an input of the at least one selected deep learning model, wherein the plurality of previously trained deep learning models is the plurality of deep learning models to be trained which is trained by performing, characterised by generating a second CT data set for training with a noise added thereto with respect to a first CT data set for training by applying a CT image noise simulator to the first CT data set for training, wherein the second CT data set for training is formed of a pair of a composite high noise CT image and a composite noise component CT image obtained based on an original image of the first CT data set for training, grouping the second CT data set for training by extracting test information from the second CT data set for training; and training a plurality of generated deep learning models to be trained using the grouped second CT data set for training, wherein in the outputting, the at least one selected deep learning model extracts a noise component CT image from the input CT image with the input CT image as an input of the at least one selected deep learning model and a predetermined value is multiplied with the extracted noise component CT image to be subtracted from the input CT image to output the CT image with a reduced noise. extracting test information from an input CT image; selecting at least one deep learning model corresponding to the test information, among a plurality of previously trained deep learning models; and outputting a CT image with a reduced noise with respect to the input CT image with the input CT image as an input of the at least one selected deep learning model, wherein the plurality of previously trained deep learning models is the plurality of deep learning models to be trained which is trained by performing, characterised by generating a second CT data set for training with a noise added thereto with respect to a first CT data set for training by applying a CT image noise simulator to the first CT data set for training, wherein the second CT data set for training is formed of a pair of a composite high noise CT image and a composite noise component CT image obtained based on an original image of the first CT data set for training, grouping the second CT data set for training by extracting test information from the second CT data set for training; and training a plurality of generated deep learning models to be trained using the grouped second CT data set for training, wherein in the outputting, the at least one selected deep learning model extracts a noise component CT image from the input CT image with the input CT image as an input of the at least one selected deep learning model and a predetermined value is multiplied with the extracted noise component CT image to be subtracted from the input CT image to output the CT image with a reduced noise.