Abstract
Light field (LF) imaging is an advanced visual perception system, which can record the intensity and direction information of light rays and provide multi-viewpoint images from a single capture. However, there is a trade-off between spatial and angular resolutions due to the restricted sensor size, which limits the wide applications of LF cameras. To address this problem, we propose a cooperative network to super-resolve LF sub-aperture images based on the multi-modality fusion. Specifically, in order to fully explore the LF information, we adopt various modalities and extract corresponding features to emphasise diverse LF characteristics. Then, we design a multi-scale fusion module to effectively integrate global and local LF features and apply frequency-aware attention mechanism to adaptively reinforce fused features. Extensive experiments demonstrate the superiority of our method on both qualitative and quantitative evaluations, with competitive execution efficiency.
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