An electronic device comprising: at least one processor (210, 2010); and at least one memory (220, 2090) configured to store instructions, wherein the at least one processor (210, 2010) is configured to execute the instructions stored in the at least one memory (220, 2090), to: obtain an input image, predict an inverse function of a monotonically increasing function for decreasing an image contrast ratio by applying the input image to a learning model (310, 350) trained by using an artificial intelligence algorithm, wherein the monotonically increasing function decreases the image contrast ratio, and obtain an output image (630, 710) based on the input image and the predicted inverse function of the monotonically increasing function, and wherein the learning model (310, 350) is trained to predict the inverse function of the monotonically increasing function for decreasing the image contrast ratio by using a training image generated by applying a target image (430, 510) having a high contrast ratio to the monotonically increasing function and the inverse function of the monotonically increasing function as training data (320, 330), wherein the monotonically increasing function comprises two parameters, wherein the at least one processor (210, 2010) is further configured to obtain a plurality of monotonically increasing functions by changing at least one of the parameters, and wherein the at least one processor (210, 2010) is further configured to obtain a plurality of training images by applying the target image (430, 510) to the plurality of monotonically increasing functions. at least one processor (210, 2010); and at least one memory (220, 2090) configured to store instructions, wherein the at least one processor (210, 2010) is configured to execute the instructions stored in the at least one memory (220, 2090), to: obtain an input image, predict an inverse function of a monotonically increasing function for decreasing an image contrast ratio by applying the input image to a learning model (310, 350) trained by using an artificial intelligence algorithm, wherein the monotonically increasing function decreases the image contrast ratio, and obtain an output image (630, 710) based on the input image and the predicted inverse function of the monotonically increasing function, and wherein the learning model (310, 350) is trained to predict the inverse function of the monotonically increasing function for decreasing the image contrast ratio by using a training image generated by applying a target image (430, 510) having a high contrast ratio to the monotonically increasing function and the inverse function of the monotonically increasing function as training data (320, 330), wherein the monotonically increasing function comprises two parameters, wherein the at least one processor (210, 2010) is further configured to obtain a plurality of monotonically increasing functions by changing at least one of the parameters, and wherein the at least one processor (210, 2010) is further configured to obtain a plurality of training images by applying the target image (430, 510) to the plurality of monotonically increasing functions. obtain an input image, predict an inverse function of a monotonically increasing function for decreasing an image contrast ratio by applying the input image to a learning model (310, 350) trained by using an artificial intelligence algorithm, wherein the monotonically increasing function decreases the image contrast ratio, and obtain an output image (630, 710) based on the input image and the predicted inverse function of the monotonically increasing function, and wherein the learning model (310, 350) is trained to predict the inverse function of the monotonically increasing function for decreasing the image contrast ratio by using a training image generated by applying a target image (430, 510) having a high contrast ratio to the monotonically increasing function and the inverse function of the monotonically increasing function as training data (320, 330), wherein the monotonically increasing function comprises two parameters, wherein the at least one processor (210, 2010) is further configured to obtain a plurality of monotonically increasing functions by changing at least one of the parameters, and wherein the at least one processor (210, 2010) is further configured to obtain a plurality of training images by applying the target image (430, 510) to the plurality of monotonically increasing functions.