公开(公告)号：US20220351334A1

公开(公告)日：2022-11-03

申请号：US17731575

申请日：2022-04-28

Applicant: Samsung Electronics Co., Ltd.

Inventor： Saikat Kumar DAS ,  Sai Kumar Reddy MANNE ,  Pankaj Kumar BAJPAI ,  Rishikesh JHA ,  Alok Shankarlal SHUKLA ,  Shashavali DOODEKULA ,  Siddharth Deepak ROHEDA

IPC: G06T5/00 ,  G06T5/50 ,  G06T7/11

Abstract: A method for performing multi-functional image restoration by an electronic device with a trained Machine Learning (ML) model is provided. The method includes receiving an image and determining channels of the image, and determining whether a number of restructuring needed for the channels is one. When the restructuring needed for the channels not one, then the method includes restructuring each channel into a first channel set, generating first inferences of the image corresponding to each channel by feeding the first channel set to the trained ML model, and generating a final inference image by combining the first inferences. When the number of restructuring needed for the channels is one, then the method includes restructuring the channels into a second channel set, and generating a second inference of the image by feeding the second channel set to the trained ML model.

公开(公告)号：US20250037248A1

公开(公告)日：2025-01-30

申请号：US18911012

申请日：2024-10-09

Applicant: SAMSUNG ELECTRONICS CO., LTD.

Inventor： Siddharth Deepak ROHEDA ,  Amit Satish UNDE ,  Alok Shankarlal SHUKLA ,  Rishikesh JHA ,  Soohyeong LEE ,  Shashavali DOODEKULA ,  Sai Kumar Reddy MANNE ,  Saikat Kumar DAS

IPC: G06T5/73

Abstract: A method of detecting blur in an input image, the method including: detecting one or more candidate blur regions of a plurality of regions in the input image; determining, a first confidence score of the one or more candidate blur regions in the input image; determining a second confidence score of a global blur in the input image; determining a third confidence score of an intentional blur in the input image; and detecting a type of blur and a strength of the type of blur in the input image based on the first confidence score of the one or more candidate blur regions, the second confidence score of the global blur, and the third confidence score of the intentional blur.