CNIPA.AI
검색으로 돌아가기
기록

FILTERING WITH SIDE-INFORMATION USING CONTEXTUALLY-DESIGNED FILTERS

발명심사 중
2조회수
20청구항 · 3 독립항
§ Ⅰ

개요

발명자

Onur Guleryuz; Debargha Mukherjee

IPC 분류

H4N 19/117H4N 19/124H4N 19/136H4N 19/82

CPC 분류

H4N19/117H4N19/124H4N19/136H4N19/82

A filter bank comprising filters is obtained. For pixels of a degraded frame, respective sets of combining scalars for combining the filters of the filter bank are obtained. For the pixels of the degraded frame, respective pixel-specific filters are obtained by combining the filters of the filter bank using the respective sets of combining scalars. A restored frame is obtained by filtering the pixels of the degraded frame using the respective pixel-specific filters. The respective sets of combining scalars may be obtained using a machine-learning model that receives the degraded frame as an input, where the machine-learning model is a convolutional neural network. The machine-learning model may be trained to minimize an error between restored frames and corresponding source frames.

원문 (중국어)

A filter bank comprising filters is obtained. For pixels of a degraded frame, respective sets of combining scalars for combining the filters of the filter bank are obtained. For the pixels of the degraded frame, respective pixel-specific filters are obtained by combining the filters of the filter bank using the respective sets of combining scalars. A restored frame is obtained by filtering the pixels of the degraded frame using the respective pixel-specific filters. The respective sets of combining scalars may be obtained using a machine-learning model that receives the degraded frame as an input, where the machine-learning model is a convolutional neural network. The machine-learning model may be trained to minimize an error between restored frames and corresponding source frames.