TY - GEN
T1 - Parallel feature pyramid network for image denoising
AU - Cho, Sung Jin
AU - Uhm, Kwang Hyun
AU - Kim, Seung Wook
AU - Ji, Seo Won
AU - Ko, Sung Jea
PY - 2020/1
Y1 - 2020/1
N2 - Image denoising is a classical and essential task in consumer electronics equipped with cameras. Recently, the convolutional neural network (CNN)-based denoising methods have been widely studied. These methods adopt single-scale features to separate image structures from the noisy observation. Single-scale features, however, have limitation in covering the full characteristics of image structures at different scales. In this paper, we propose a novel denoising network that makes use of the multi-scale feature pyramid where each feature map represents the characteristics of image structure at different scales. We then combine these multi-scale features to obtain the contextual information and utilize it to effectively generate clear denoised results. Experimental results show that our network achieves superior performance to other conventional methods.
AB - Image denoising is a classical and essential task in consumer electronics equipped with cameras. Recently, the convolutional neural network (CNN)-based denoising methods have been widely studied. These methods adopt single-scale features to separate image structures from the noisy observation. Single-scale features, however, have limitation in covering the full characteristics of image structures at different scales. In this paper, we propose a novel denoising network that makes use of the multi-scale feature pyramid where each feature map represents the characteristics of image structure at different scales. We then combine these multi-scale features to obtain the contextual information and utilize it to effectively generate clear denoised results. Experimental results show that our network achieves superior performance to other conventional methods.
UR - http://www.scopus.com/inward/record.url?scp=85082578965&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85082578965&partnerID=8YFLogxK
U2 - 10.1109/ICCE46568.2020.9043111
DO - 10.1109/ICCE46568.2020.9043111
M3 - Conference contribution
AN - SCOPUS:85082578965
T3 - Digest of Technical Papers - IEEE International Conference on Consumer Electronics
BT - 2020 IEEE International Conference on Consumer Electronics, ICCE 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2020 IEEE International Conference on Consumer Electronics, ICCE 2020
Y2 - 4 January 2020 through 6 January 2020
ER -