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Fast: Flow-Assisted Shearlet Transform for Densely-Sampled Light Field Reconstruction

Tutkimustuotosvertaisarvioitu

Yksityiskohdat

AlkuperäiskieliEnglanti
Otsikko2019 IEEE International Conference on Image Processing (ICIP)
KustantajaIEEE
Sivut3741-3745
Sivumäärä5
ISBN (elektroninen)978-1-5386-6249-6
ISBN (painettu)978-1-5386-6250-2
DOI - pysyväislinkit
TilaJulkaistu - syyskuuta 2019
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisussa
TapahtumaIEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING -
Kesto: 1 tammikuuta 1900 → …

Julkaisusarja

NimiIEEE International Conference on Image Processing
ISSN (painettu)1522-4880
ISSN (elektroninen)2381-8549

Conference

ConferenceIEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING
Ajanjakso1/01/00 → …

Tiivistelmä

Shearlet Transform (ST) is one of the most effective methods for Densely-Sampled Light Field (DSLF) reconstruction from a Sparsely-Sampled Light Field (SSLF). However, ST requires a precise disparity estimation of the SSLF. To this end, in this paper a state-of-the-art optical flow method, i.e. PWC-Net, is employed to estimate bidirectional disparity maps between neighboring views in the SSLF. Moreover, to take full advantage of optical flow and ST for DSLF reconstruction, a novel learning-based method, referred to as Flow-Assisted Shearlet Transform (FAST), is proposed in this paper. Specifically, FAST consists of two deep convolutional neural networks, i.e. disparity refinement network and view synthesis network, which fully leverage the disparity information to synthesize novel views via warping and blending and to improve the novel view synthesis performance of ST. Experimental results demonstrate the superiority of the proposed FAST method over the other state-of-the-art DSLF reconstruction methods on nine challenging real-world SSLF sub-datasets with large disparity ranges (up to 26 pixels).

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