[논문]Light Field Super-Resolution via Adaptive Feature Remixing

Ko, Keunsoo; Koh, Yeong Jun; Chang, Soonkeun; Kim, Chang-Su

doi:10.1109/tip.2021.3069291

[해외논문] Light Field Super-Resolution via Adaptive Feature Remixing

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society, v.30, 2021년, pp.4114 - 4128

Ko, Keunsoo (Korea University, School of Electrical Engineering, Seoul, South Korea) , Koh, Yeong Jun (Chungnam National University, Daejeon, South Korea) , Chang, Soonkeun (Samsung Electronics Company Ltd., Hwaseong, South Korea) , Kim, Chang-Su (Korea University, School of Electrical Engineering, Seoul, South Korea)

Abstract ▼ AI-Helper

A novel light field super-resolution algorithm to improve the spatial and angular resolutions of light field images is proposed in this work. We develop spatial and angular super-resolution (SR) networks, which can faithfully interpolate images in the spatial and angular domains regardless of the angular coordinates. For each input image, we feed adjacent images into the SR networks to extract multi-view features using a trainable disparity estimator. We concatenate the multi-view features and remix them through the proposed adaptive feature remixing (AFR) module, which performs channel-wise pooling. Finally, the remixed feature is used to augment the spatial or angular resolution. Experimental results demonstrate that the proposed algorithm outperforms the state-of-the-art algorithms on various light field datasets. The source codes and pre-trained models are available at https://github.com/keunsoo-ko/ LFSR-AFR

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DOI : 10.1109/TIP.2021.3069291
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