Multi-Stream Progressive Restoration for Low-Light Light Field Enhancement and Denoising

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Multi-Stream Progressive Restoration for Low-Light Light Field Enhancement and Denoising

Xianglang Wang; Youfang Lin; Shuo Zhang

Light Fields (LFs) are easily degraded by noise and low light. Low light LF enhancement and denoising are more challenging than single image tasks because the epipolar information among views should be taken into consideration. In this work, we propose a multiple stream progressive restoration network to restore the whole LF in just one forward pass. To make full use of the multiple views supplementary information and preserve the epipolar information, we design three types of input composed of view stacking. Each type of input corresponds to an restoration stream and provides specific complementary information. In addition, the weights are shared for each type of input in order to better maintain the epipolar information among views. To fully utilize the supplementary information, we then design a multi-stream interaction module to aggregate features from different restoration streams. Finally, the multiple stages restoration is introduced to reconstruct the LF progressively. We carry out extensive experiments to demonstrate that our model outperforms the state-of-the-art techniques on real world low light LF dataset and synthetic noisy LF dataset.

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