Method and device with image processing

An electronic device comprising:

• one or more processors respectively comprising processing circuitry;
• and
• a memory storing executable code, which upon execution by the one or more processors, configures the one or more processors to:
• receive a blur image generated by capturing a target scene along a three-dimensional (3D) camera trajectory during an exposure time;
• estimate, using a neural network-based motion estimation model, camera poses corresponding to image components captured at camera positions on the 3D camera trajectory, wherein the image components form the blur image;
• and
• generate, based on the camera poses, vector fields representing a difference between an initial image component captured at a starting point of the 3D camera trajectory and the image components captured at the camera positions.

A method for generating a three-dimensional (3D) aware vector field for a blur image, the method comprising:

• capturing a blur image of a target scene along a 3D camera trajectory during an exposure interval;
• estimating a vector field representing differences between an initial image component captured at a starting point of the 3D camera trajectory and subsequent image components captured at camera positions along the 3D camera trajectory, the estimating being performed using a neural network-based motion estimation model;
• adjusting one or more of an amplitude and a phase of the vector field to generate a controllable vector field;
• and
• using the controllable vector field to configure a training dataset for a deblur model, wherein the training dataset comprises a training data pair including the blur image and a sharp image of the target scene by applying the controllable vector field.

An electronic device comprising:

• one or more processors;
• and
• a memory storing executable code which, when executed by the one or more processors, cause the electronic device to:
• capture a blur image of a target scene along a 3D camera trajectory during an exposure time;
• estimate a vector field representing differences between an initial image component captured at a starting point of the 3D camera trajectory and subsequent image components captured at camera positions along the 3D camera trajectory using a neural network-based motion estimation model;
• adjust one or more of an amplitude and a phase of the vector field to generate a controllable vector field;
• and
• use the controllable vector field to configure a training dataset for a deblur model, wherein the training dataset comprises a training data pair including the blur image and a sharp image by applying the controllable vector field.