An electronic apparatus comprising: a memory storing one or more instructions; and a processor , electrically connected to the memory , and configured to execute the one or more instructions to: obtain, from an input image, a noise map corresponding to the input image; provide the input image to an input layer of a learning network model (320-1, 320-2) including a plurality of layers, the learning network model (320-1, 320-2) being an artificial intelligence, AI, model that is obtained by learning, through an AI algorithm, a relationship between a plurality of sample images (520-1, 520-2, 520-3, 520-4), a respective noise map (530-1, 530-2, 530-3, 530-4) of each of the plurality of sample images (520-1, 520-2, 520-3, 520-4), and an original image corresponding to the plurality of sample images (520-1, 520-2, 520-3, 520-4); provide the noise map to at least one intermediate layer among the plurality of layers; and obtain an output image based on a result from providing the input image and the noise map to the learning network model (320-1, 320-2), the output image having improved quality relative to the input image, wherein each of the plurality of sample images (520-1, 520-2, 520-3, 520-4) is a compression image in which an original image is compressed, and wherein the noise map (530-1, 530-2, 530-3, 530-4) for the respective sample images (520-1, 520-2, 520-3, 520-4) is a noise map (530-1, 530-2, 530-3, 530-4) obtained from the respective sample images (520-1, 520-2, 520-3, 520-4) and an original image corresponding to the respective sample images (520-1, 520-2, 520-3, 520-4). a memory storing one or more instructions; and a processor , electrically connected to the memory , and configured to execute the one or more instructions to: obtain, from an input image, a noise map corresponding to the input image; provide the input image to an input layer of a learning network model (320-1, 320-2) including a plurality of layers, the learning network model (320-1, 320-2) being an artificial intelligence, AI, obtain, from an input image, a noise map corresponding to the input image; provide the input image to an input layer of a learning network model (320-1, 320-2) including a plurality of layers, the learning network model (320-1, 320-2) being an artificial intelligence, AI, model that is obtained by learning, through an AI algorithm, a relationship between a plurality of sample images (520-1, 520-2, 520-3, 520-4), a respective noise map (530-1, 530-2, 530-3, 530-4) of each of the plurality of sample images (520-1, 520-2, 520-3, 520-4), and an original image corresponding to the plurality of sample images (520-1, 520-2, 520-3, 520-4); provide the noise map to at least one intermediate layer among the plurality of layers; and obtain an output image based on a result from providing the input image and the noise map to the learning network model (320-1, 320-2), the output image having improved quality relative to the input image, provide the noise map to at least one intermediate layer among the plurality of layers; and obtain an output image based on a result from providing the input image and the noise map to the learning network model (320-1, 320-2), the output image having improved quality relative to the input image, wherein each of the plurality of sample images (520-1, 520-2, 520-3, 520-4) is a compression image in which an original image is compressed, and wherein the noise map (530-1, 530-2, 530-3, 530-4) for the respective sample images (520-1, 520-2, 520-3, 520-4) is a noise map (530-1, 530-2, 530-3, 530-4) obtained from the respective sample images (520-1, 520-2, 520-3, 520-4) and an original image corresponding to the respective sample images (520-1, 520-2, 520-3, 520-4). <EOS>