%0 Conference Proceedings
%T Non-local Second-Order Attention Network for Single Image Super Resolution
%+ Trinity College Dublin
%A Lyn, Jiawen
%A Yan, Sen
%< avec comité de lecture
%( Lecture Notes in Computer Science
%B 4th International Cross-Domain Conference for Machine Learning and Knowledge Extraction (CD-MAKE)
%C Dublin, Ireland
%Y Andreas Holzinger
%Y Peter Kieseberg
%Y A Min Tjoa
%Y Edgar Weippl
%I Springer International Publishing
%3 Machine Learning and Knowledge Extraction
%V LNCS-12279
%P 267-279
%8 2020-08-25
%D 2020
%R 10.1007/978-3-030-57321-8_15
%K Super resolution
%K Deep neural network
%K Deep learning
%Z Computer Science [cs]
%Z Humanities and Social Sciences/Library and information sciencesConference papers
%X Single image super-resolution is a ill-posed problem which aims to characterize the texture pattern given a blurry and low-resolution image sample. Convolution neural network recently are introduced into super resolution to tackle this problem and further bringing forward progress in this field. Although state-of-the-art studies have obtain excellent performance by designing the structure and the way of connection in the convolution neural network, they ignore the use of high-order data to train more power model. In this paper, we propose a non-local second-order attention network for single image super resolution, which make the full use of the training data and further improve performance by non-local second-order attention. This attention scheme does not only provide a guideline to design the network, but also interpretable for super-resolution task. Extensive experiments and analyses have demonstrated our model exceed the state-of-the-arts models with similar parameters.
%G English
%Z TC 5
%Z TC 8
%Z TC 12
%Z WG 8.4
%Z WG 8.9
%Z WG 12.9
%2 https://inria.hal.science/hal-03414726/document
%2 https://inria.hal.science/hal-03414726/file/497121_1_En_15_Chapter.pdf
%L hal-03414726
%U https://inria.hal.science/hal-03414726
%~ SHS
%~ IFIP-LNCS
%~ IFIP
%~ IFIP-TC
%~ IFIP-TC5
%~ IFIP-WG
%~ IFIP-TC12
%~ IFIP-TC8
%~ IFIP-WG8-4
%~ IFIP-WG8-9
%~ IFIP-CD-MAKE
%~ IFIP-WG12-9
%~ IFIP-LNCS-12279