TY - GEN
T1 - High Reflection Removal Using CNN with Detection and Estimation
AU - Funahashi, Isana
AU - Yamashita, Naoki
AU - Yoshida, Taichi
AU - Ikehara, Masaaki
N1 - Publisher Copyright:
© 2021 APSIPA.
PY - 2021
Y1 - 2021
N2 - In this paper, we propose a method of reflection removal that reduces high intensity reflection for single image. Various methods of reflection removal have been proposed, but they fail to reduce the high reflections due to their assumption. To tackle this issue, the proposed method detects the target areas with high reflections by the proposed convolutional neural network (CNN) model and estimates their background information by inpainting. It is observed that the reflection is strongly blurred because of its physical property, and hence the proposed CNN model utilizes edge features of pixels for the detection. In simulation, we compare state-of-the-art methods of reflection removal with and without the proposed method for natural images, and the proposed method improves peak signal-to-noise ratio and perceptual quality.
AB - In this paper, we propose a method of reflection removal that reduces high intensity reflection for single image. Various methods of reflection removal have been proposed, but they fail to reduce the high reflections due to their assumption. To tackle this issue, the proposed method detects the target areas with high reflections by the proposed convolutional neural network (CNN) model and estimates their background information by inpainting. It is observed that the reflection is strongly blurred because of its physical property, and hence the proposed CNN model utilizes edge features of pixels for the detection. In simulation, we compare state-of-the-art methods of reflection removal with and without the proposed method for natural images, and the proposed method improves peak signal-to-noise ratio and perceptual quality.
KW - Single image reflection removal
KW - convolutional neural network
KW - image inpainting
UR - http://www.scopus.com/inward/record.url?scp=85126709632&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85126709632&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85126709632
T3 - 2021 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2021 - Proceedings
SP - 1381
EP - 1385
BT - 2021 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2021 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2021 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2021
Y2 - 14 December 2021 through 17 December 2021
ER -