公开(公告)号：US11790500B2

公开(公告)日：2023-10-17

申请号：US17482433

申请日：2021-09-23

Applicant: KOREA ADVANCED INSTITUTE OF SCIENCE AND TECHNOLOGY

Inventor： Mun Churl Kim ,  Soo Ye Kim ,  Dae Eun Kim

IPC: G06T5/00 ,  G06F17/11 ,  G06N3/02 ,  G06N3/08 ,  G06T5/50

CPC classification number: G06T5/009 ,  G06F17/11 ,  G06N3/02 ,  G06N3/08 ,  G06T5/50 ,  G06T2207/10016 ,  G06T2207/10024 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/20208

Abstract: In this invention, we propose a convolutional neural network (CNN) based architecture designed for the ITM to HDR consumer displays, called ITM-CNN, and its training strategy for enhancing the performance based on image decomposition using the guided filter. We demonstrate the benefits of decomposing the image by experimenting with various architectures and also compare the performance for different training strategies. To the best of our knowledge, this invention first presents the ITM problem using CNNs for HDR consumer displays, where the network is trained to restore lost details and local contrast. Our ITM-CNN can readily up-convert LDR images for direct viewing on an HDR consumer medium, and is a very powerful means to solve the lack of HDR video contents with legacy LDR videos.

公开(公告)号：US10664961B2

公开(公告)日：2020-05-26

申请号：US15633763

申请日：2017-06-27

Applicant: SILICON WORKS CO., LTD. ,  Korea Advanced Institute of Science and Technology

Inventor： Yong Woo Kim ,  Sang Yeon Kim ,  Woo Suk Ha ,  Mun Churl Kim ,  Dae Eun Kim

IPC: G06K9/00 ,  G06K9/40 ,  G06T5/00 ,  G06T7/90 ,  G06T5/10 ,  G06T5/20

Abstract: The present invention provides a technology that separates a low-contrast-ratio image into sublayer images, classifies each sublayer image into several categories in accordance with the characteristics of each sublayer image, and learns a transformation matrix representing a relationship between the low-contrast-ratio image and a high-contrast-ratio image for each category. In addition, the present invention provides a technology that separates an input low-contrast-ratio image into sublayer images, selects a category corresponding to each sublayer image, and applies a learned transformation matrix to generate a high.