公开(公告)号：US20210183020A1

公开(公告)日：2021-06-17

申请号：US17119336

申请日：2020-12-11

Applicant: SAMSUNG ELECTRONICS CO, LTD.

Inventor： Tejpratap Venkata Subbu Lakshmi GOLLANAPALLI ,  Kuladeep MARUPALLI

IPC: G06T5/00 ,  G06T7/73 ,  G06T5/20 ,  G06T9/00

Abstract: A method for deblurring a blurred image includes encoding, by at least one processor, a blurred image at a plurality of stages of encoding to obtain an encoded image at each of the plurality of stages; decoding, by the at least one processor, an encoded image obtained from a final stage of the plurality of stages of encoding by using an encoding feedback from each of the plurality of stages and a machine learning (ML) feedback from at least one ML model; and generating, by the at least one processor, a deblurred image in which at least one portion of the blurred image is deblurred based on a result of the decoding.

公开(公告)号：US20230252602A1

公开(公告)日：2023-08-10

申请号：US17978458

申请日：2022-11-01

Applicant: SAMSUNG ELECTRONICS CO, LTD.

Inventor： Tejpratap Venkata Subbu Lakshmi GOLLANAPALLI ,  Raja KUMAR ,  Dewashish DHARKAR

IPC: G06T3/40

CPC classification number: G06T3/4046

Abstract: Embodiments of the disclosure provide a method and device for efficiently reducing dimensions of an image frame by an electronic device. The method includes: receiving the image frame; transforming the image frame from a spatial domain comprising a first plurality of channels to a non-spatial domain comprising a second plurality of channels, where a number of the second plurality of channels is greater than a number of the first plurality of channels; removing channels comprising irrelevant information from among the second plurality of channels using an AI engine to generate a low-resolution image frame in the non-spatial domain; and providing the low-resolution image frame to a neural network for a faster and accurate inference of the image frame.

公开(公告)号：US20230068381A1

公开(公告)日：2023-03-02

申请号：US17961453

申请日：2022-10-06

Applicant: SAMSUNG ELECTRONICS CO., LTD.

Inventor： Tejpratap Venkata Subbu Lakshmi GOLLANAPALLI ,  Arun ABRAHAM ,  Raja KUMAR ,  Pradeep NELAHONNE SHIVAMURTHAPPA ,  Vikram Nelvoy RAJENDIRAN ,  Prasen Kumar SHARMA

IPC: G06N3/08 ,  G06V10/82

Abstract: Various embodiments of the disclosure disclose a method for quantizing a Deep Neural Network (DNN) model in an electronic device. The method includes: estimating, by the electronic device, an activation range of each layer of the DNN model using self-generated data (e.g. retro image, audio, video, etc.) and/or a sensitive index of each layer of the DNN model; quantizing, by the electronic device, the DNN model based on the activation range and/or the sensitive index; and allocating, by the electronic device, a dynamic bit precision for each channel of each layer of the DNN model to quantize the DNN model.