Light field image restoration in low-light environment

Zhou Ge; Li Song; Edmund Y. Lam

doi:10.1117/12.2580033

8 November 2020 Light field image restoration in low-light environment

Zhou Ge, Li Song, Edmund Y. Lam

Proceedings Volume 11525, SPIE Future Sensing Technologies; 115251H (2020) https://doi.org/10.1117/12.2580033
Event: SPIE Future Sensing Technologies, 2020, Online Only

Abstract

Light field (LF) imaging provides rich spatial and angular information, but is problematic in low-light environment as the images suffer from low contrast and visibility. In this paper, we present a learning-based method to enhance low-light LF images. A high-dimensional convolutional neural network (CNN) is introduced to extract the spatio-angular features from the LF. The network operates directly on the four-dimensional LF data rather than on individual sub-aperture images, avoiding the loss of geometric information. Color compensation is then performed on the enhanced LF images coming from the high-dimensional CNN to reduce color distortion. The experimental results show that the proposed method achieves noticeable improvement compared with state-of-the-art low-light image restoration techniques in both visual inspection and objective assessments.

Conference Presentation

Citation Download Citation

Zhou Ge, Li Song, and Edmund Y. Lam "Light field image restoration in low-light environment", Proc. SPIE 11525, SPIE Future Sensing Technologies, 115251H (8 November 2020); https://doi.org/10.1117/12.2580033

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