Techniques for Removing a Distraction in an Image

A computer-implemented method for configuring an image editing operator for reducing a distractor in raw image data, the method comprising:

• accessing, by one or more computing devices, the raw image data and a mask, wherein the mask indicates a region of interest associated with the raw image data;
• processing, by the one or more computing devices, the raw image data and the mask with the image editing operator to generate processed image data;
• generating, by the one or more computing devices, a saliency map for at least the processed image data within the region of interest;
• and
• modifying, by the one or more computing devices, one or more parameter values of the image editing operator for processing the raw image data into the processed image data based at least in part on a saliency loss function to reduce the distractor in the raw image.

A computing system, comprising:

• one or more processors;
• one or more non-transitory computer-readable image that collectively store:
• an image editing operator, wherein the image editing operator is configured to process image data;
• a trained saliency model, wherein the trained saliency model is configured to generate a saliency map;
• and
• instructions that, when executed by the one or more processors, cause the computing system to perform operations, the operations comprising:
• accessing raw image data and a mask, wherein the mask indicates a region of interest associated with the raw image data;
• processing, using the image editing operator, the raw image data and the mask with the image editing operator to generate processed image data;
• generating, using the trained saliency model, a saliency map for at least the processed image data within the region of interest;
• and
• modifying one or more parameter values of the image editing operator for processing the raw image data into the processed image data based at least in part on a saliency loss function to reduce the distractor in the raw image.

One or more non-transitory computer-readable media that collectively store a machine-learned image editing operator, wherein the image editing operator has been learned by performance of operations, the operations comprising:

• accessing raw image data and a mask, wherein the mask indicates a region of interest associated with the raw image data;
• processing the raw image data and the mask with the image editing operator to generate processed image data;
• generating a saliency map for at least the processed image data within the region of interest;
• and
• modifying one or more parameter values of the image editing operator for processing the raw image data into the processed image data based at least in part on a saliency loss function to reduce the distractor in the raw image.