An image processing apparatus, comprising: a memory configured to store at least one command; and a processor electrically connected to the memory, wherein the processor is further configured to: obtain the input image by deteriorating at least one characteristic of a target image, wherein the target image is one among a plurality of training images, obtain a first output image output from a first training network model based on the input image as the only input to the first training network model, obtain a second output image output from a second training network model based on the input image as the only input to the second training network model, and obtain a reconstructed image based on the first output image and the second output image, wherein the input image is a low resolution image, and the first and second output images (20-1, 20-2) are higher resolution images, wherein each of the first and second training network models (1000, 2000) are neural networks including a plurality of layers and a plurality of parameters obtained from deep learning, wherein the first training network model is configured output the first output image based on fixed parameters obtained through pre-training on a plurality of sample images, such that no additional training is performed, wherein the second training network model is trained to minimize a difference between the target image corresponding to the input image and the reconstructed image, and wherein the target image is of a higher resolution than the input image and is not based on the first and second output images (20-1, 20-2). a memory configured to store at least one command; and a processor electrically connected to the memory, wherein the processor is further configured to: obtain the input image by deteriorating at least one characteristic of a target image, wherein the target image is one among a plurality of training images, obtain a first output image output from a first training network model based on the input image as the only input to the first training network model, obtain a second output image output from a second training network model based on the input image as the only input to the second training network model, and obtain a reconstructed image based on the first output image and the second output image, wherein the input image is a low resolution image, and the first and second output images (20-1, 20-2) are higher resolution images, wherein each of the first and second training network models (1000, 2000) are neural networks including a plurality of layers and a plurality of parameters obtained from deep learning, wherein the first training network model is configured output the first output image based on fixed parameters obtained through pre-training on a plurality of sample images, such that no additional training is performed, wherein the second training network model is trained to minimize a difference between the target image corresponding to the input image and the reconstructed image, and wherein the target image is of a higher resolution than the input image and is not based on the first and second output images (20-1, 20-2). obtain the input image by deteriorating at least one characteristic of a target image, wherein the target image is one among a plurality of training images, obtain a first output image output from a first training network model based on the input image as the only input to the first training network model, obtain a second output image output from a second training network model based on the input image as the only input to the second training network model, and obtain a reconstructed image based on the first output image and the second output image, wherein the input image is a low resolution image, and the first and second output images (20-1, 20-2) are higher resolution images, wherein each of the first and second training network models (1000, 2000) are neural networks including a plurality of layers and a plurality of parameters obtained from deep learning, wherein the first training network model is configured output the first output image based on fixed parameters obtained through pre-training on a plurality of sample images, such that no additional training is performed, wherein the second training network model is trained to minimize a difference between the target image corresponding to the input image and the reconstructed image, and wherein the target image is of a higher resolution than the input image and is not based on the first and second output images (20-1, 20-2).