Apparatus for white balance editing, the apparatus comprising: a memory storing instructions; and at least one processor configured to execute the instructions to: obtain an input image having a corrected white balance that is obtained by correcting an original white balance via image signal processing; obtain, using a first neural network, an intermediate representation of the obtained input image, wherein the intermediate representation has the original white balance; and obtain, using a second neural network, a first output image having a first white balance different than the original white balance, based on the obtained intermediate representation, and obtain, using a third neural network, a second output image having a second white balance different than the original white balance and the first white balance, based on the obtained intermediate representation, and wherein the first neural network is an encoder part of a U-net architecture, and wherein the second and third neural networks are identically structured decoder parts of the U-net architecture, and wherein a multi-scale skip connection connects layers of the first neural network with layers of the second and third neural networks a memory storing instructions; and at least one processor configured to execute the instructions to: obtain an input image having a corrected white balance that is obtained by correcting an original white balance via image signal processing; obtain, using a first neural network, an intermediate representation of the obtained input image, wherein the intermediate representation has the original white balance; and obtain, using a second neural network, a first output image having a first white balance different than the original white balance, based on the obtained intermediate representation, and obtain, using a third neural network, a second output image having a second white balance different than the original white balance and the first white balance, based on the obtained intermediate representation, and wherein the first neural network is an encoder part of a U-net architecture, and wherein the second and third neural networks are identically structured decoder parts of the U-net architecture, and wherein a multi-scale skip connection connects layers of the first neural network with layers of the second and third neural networks obtain an input image having a corrected white balance that is obtained by correcting an original white balance via image signal processing; obtain, using a first neural network, an intermediate representation of the obtained input image, wherein the intermediate representation has the original white balance; and obtain, using a second neural network, a first output image having a first white balance different than the original white balance, based on the obtained intermediate representation, and obtain, using a third neural network, a second output image having a second white balance different than the original white balance and the first white balance, based on the obtained intermediate representation, and wherein the first neural network is an encoder part of a U-net architecture, and wherein the second and third neural networks are identically structured decoder parts of the U-net architecture, and wherein a multi-scale skip connection connects layers of the first neural network with layers of the second and third neural networks