Electronic device and controlling method of electronic device

1. An electronic device comprising:

at least one memory configured to store at least one instruction; and

at least one processor configured to execute the at least one instruction to:

obtain an original image and a corrected image, wherein the corrected image is obtained from the original image based on settings of a user;

provide the original image and the corrected image to a neural network model;

obtain, by using the neural network model, information for at least one correction parameter by identifying the at least one correction parameter to be applied to the original image to generate the corrected image;

receive a user command to correct a first image; and

obtain a second image in which the first image is corrected based on the information for the at least one correction parameter.

2. The electronic device of claim 1 , wherein the corrected image is obtained by correcting the original image.

3. The electronic device of claim 1 , wherein the information for the at least one correction parameter is obtained by identifying pixel values of the corrected image.

4. The electronic device of claim 1 , wherein the at least one processor comprises an AI processor for controlling operations of the neural network model.

5. The electronic device of claim 1 , wherein the neural network model comprises an implementor configured to obtain the information for the at least one correction parameter and a comparator configured to compare a plurality of images, and

wherein the at least one processor is further configured to:

obtain the information for the at least one correction parameter by inputting the original image to the implementor;

obtain a fifth image by correcting the original image based on the information for the at least one correction parameter;

obtain second feedback information based on a difference between a pixel value of the corrected image and a pixel value of the fifth image by inputting the corrected image and the fifth image to the comparator; and

train the implementor based on the second feedback information.

6. The electronic device of claim 5 , wherein the at least one processor is further configured to:

based on receiving the user command, input the first image to the neural network model; and

obtain the second image by correcting the original image based on the information for the at least one correction parameter.

7. The electronic device of claim 1 , further comprising a display,

wherein the at least one processor is further configured to execute the at least one instruction to:

based on obtaining the second image, control the display to display a first user interface (UI) element to select whether to correct the second image based on a user setting with respect to a correction parameter;

based on receiving a third user command for selecting to correct the second image through the first UI element, control the display to display a second UI element to select at least one parameter associated with correction of the second image; and

based on receiving a fourth user command for selecting the at least one parameter associated with correction of the second image through the second UI element, obtain a fifth image by correcting the second image.

8. The electronic device of claim 1 , wherein the at least one processor is further configured to execute the at least one instruction to:

obtain first type information associated with a type of the original image; and

train the neural network model based on the original image, the corrected image, and the first type information.

9. The electronic device of claim 8 , wherein the at least one processor is further configured to execute the at least one instruction to:

based on obtaining the first image, obtain second type information associated with a type of the first image; and

based on receiving the user command to correct the first image, obtain the second image by correcting the first image using the neural network model based on the first image and the second type information.

10. The electronic device of claim 1 , wherein the at least one processor is further configured to execute the at least one instruction to identify the corrected image based on a first user command indicating the corrected image.

11. The electronic device of claim 1 , further comprising a display,

wherein the at least one processor is further configured to execute the at least one instruction to:

control the display to display a plurality of images; and

identify the corrected image based on a first user command indicating the corrected image, from among the plurality of images.

12. A method for controlling of an electronic device, the method comprising:

obtaining an original image and a corrected image, wherein the corrected image is obtained from the original image based on settings of a user;

providing the original image and the corrected image to a neural network model;

obtaining, by using the neural network model, information for at least one correction parameter to be applied to the original image to generate the corrected image;

receiving a user command to correct a first image; and

obtaining a second image in which the first image is corrected based on the information for the at least one correction parameter.

13. The method of claim 12 , further comprising obtaining the corrected image by correcting the original image.

14. The method of claim 12 , further comprising identifying pixel values of the corrected image to obtain the information for the at least one correction parameter.

15. The method of claim 12 , further comprising controlling an artificial intelligence (AI) processor to control operations of the neural network model.

16. The method of claim 12 , further comprising:

obtaining the information for the at least one correction parameter by inputting the original image to the neural network model;

obtaining a fifth image by correcting the original image based on the information for the at least one correction parameter;

obtaining second feedback information based on a difference between a pixel value of the corrected image and a pixel value of the fifth image; and

training the neural network model based on the second feedback information.

17. The method of claim 16 , further comprising:

based on receiving the user command, inputting the first image to the neural network model; and

obtaining the second image by correcting the original image based on the information for the at least one correction parameter.

18. The method of claim 12 , further comprising,

based on obtaining the second image, controlling a display to display a first user interface (UI) element to select whether to correct the second image based on a user setting with respect to a correction parameter;

based on receiving a third user command for selecting to correct the second image through the first UI element, controlling the display to display a second UI element to select at least one parameter associated with correction of the second image; and

based on receiving a fourth user command for selecting the at least one parameter associated with correction of the second image through the second UI element, obtaining a fifth image by correcting the second image.

19. The method of claim 12 , further comprising:

obtaining first type information associated with a type of the original image;

training the neural network model based on the original image, the corrected image, and the first type information;

based on obtaining the first image, obtaining second type information associated with a type of the first image; and

based on receiving the user command to correct the first image, obtaining the second image by correcting the first image using the neural network model based on the first image and the second type information.

20. A non-transitory computer readable recording medium storing a program which is executable by processor to perform a method for controlling an electronic device, the method comprising:

obtaining an original image and a corrected image, wherein the corrected image is obtained from the original image based on settings of a user;

provide the original image and the corrected image to a neural network model;

obtaining, by using the neural network model, information for at least one correction parameter to be applied to the original image to generate the corrected image;

receiving a user command to correct a first image; and

obtaining a second image in which the first image is corrected based on the information for the at least one correction parameter.