Dynamic Light Field Reconstruction Based on Gradient Descent Deep Equilibrium Model(Invited)
In response to snapshot compressive imaging(SCI)in dynamic light field temporal domain,a method called dynamic light field deep equilibrium(DLFDEQ)was proposed to reconstruct high-quality dynamic light field image frames(5D data)from known encoding patterns and acquired snapshot compressed measurements(4D data).First,based on compressive sensing,the same compression encoding for each viewpoint of dynamic light field image frames in the temporal domain was adopted.Second,the reconstruction process of compressive measurements was modeled as an inverse problem with an implicit regularization term.Finally,the inverse problem was solved through a deep equilibrium model based on gradient descent(DEQ-GD).The DEQ-GD model allows for the stable reconstruction of the required dynamic light field image frames from snapshot compressive measurements.Experimental results demonstrate that proposed method can recover a 5×5 viewpoints dynamic light field composed of 4 frames of images from a single snapshot light field measurement of a 5×5 viewpoints.Compared with the current state-of-the-art methods,proposed method demonstrates stronger robustness and preserves more accurate details in the reconstructed dynamic light field image frames.By repeatedly capturing and recovering these compressive measurement values,the temporal frame rate of reconstructed image is 4 times of the original camera frame rate.
computational photographycompressive imagingdynamic light fielddeep equilibrium model
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