Paper
25 March 2023 Light field image super-resolution using selective kernel convolution
Author Affiliations +
Proceedings Volume 12592, International Workshop on Advanced Imaging Technology (IWAIT) 2023; 1259206 (2023) https://doi.org/10.1117/12.2670321
Event: International Workshop on Advanced Imaging Technology (IWAIT) 2023, 2023, Jeju, Korea, Republic of
Abstract
Over the years, many methods have emerged to solve the super-resolution problem of light field images, and among them, those methods based on deep learning are noted quite attractive recently. Although the features extracted from epipolar domain for the super-resolution of light field images are actively investigated due to their potential capability of well capturing the relationship between spatial and angular domains, we note that spatial features are still the most important foundation in feature extraction. In this paper, we design a network, named as LFSelectSR, employing multiple convolutional kernels to fully extract spatial features and introduce a dynamic selection mechanism that can extract the most valuable spatial features. By training and testing the network using well-known datasets, we demonstrate its excellent performance of achieving the level of state-of-the-arts under certain conditions.
© (2023) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Yuduo Zhang, Vinh Van Duong, Jonghoon Yim, and Byeungwoo Jeon "Light field image super-resolution using selective kernel convolution", Proc. SPIE 12592, International Workshop on Advanced Imaging Technology (IWAIT) 2023, 1259206 (25 March 2023); https://doi.org/10.1117/12.2670321
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KEYWORDS
Feature extraction

Convolution

Super resolution

Spatial learning

Education and training

Spatial resolution

Image quality

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