Electronic device for performing vision perception from image acquired using meta lens, and operating method thereof

A method, performed by an electronic device, of obtaining a perception result of an object from an image obtained through a meta lens, the method comprising:

• obtaining a coded image by receiving phase-modulated light reflected from the object and transmitted by the meta lens and converting the phase-modulated light into an electrical signal;
• and
• inputting the coded image to an artificial intelligence model and obtaining a label indicating a perception result of the object through inferencing using the artificial intelligence model,
• wherein the artificial intelligence model is a neural network model trained to obtain a simulated image by inputting an RGB image to a model that reflects optical characteristics of the meta lens and to output a label indicating ground truth of the input RGB image as a perception result of the simulated image, and
• wherein the artificial intelligence model is trained to minimize a loss value by applying a similarity value numerically representing a degree of similarity between the RGB image and the simulated image as the loss value.

A non-transitory computer-readable medium storing one or more programs, the one or more programs comprising instructions which, when executed by at least one processor of an electronic device, cause the electronic device to:

• obtain a coded image by receiving phase-modulated light reflected from an object and transmitted by a meta lens and converting the phase-modulated light into an electrical signal;
• and
• input the coded image to an artificial intelligence model and obtain a label indicating a perception result of the object through inferencing using the artificial intelligence model,
• wherein the artificial intelligence model is a neural network model trained to obtain a simulated image by inputting an RGB image to a model that reflects optical characteristics of the meta lens and to output a label indicating ground truth of the input RGB image as a perception result of the simulated image, and
• wherein the artificial intelligence model is trained to minimize a loss value by applying a similarity value numerically representing a degree of similarity between the RGB image and the simulated image as the loss value.