基于梯度下降深度均衡模型的动态光场重建(特邀)

Dynamic Light Field Reconstruction Based on Gradient Descent Deep Equilibrium Model(Invited)

针对动态光场时域快照压缩成像(SCI),提出了一种名为动态光场深度均衡(DLFDEQ)的方法,该方法能够从已知的编码模式和获得的快照压缩测量(4D数据)中重建高质量的动态光场图像帧(5D数据)。首先基于压缩传感,在时域内对动态光场图像帧的每个视点采用相同的压缩编码,然后将压缩测量的重建过程建模为一个带有隐式正则化项的逆问题,最后通过基于梯度下降的深度均衡(DEQ-GD)模型来解决此逆问题。DEQ-GD模型可以从快照压缩测量中稳定地重建所需的动态光场图像帧。实验结果表明,所提方法可以从视点数为5×5的单次快照光场测量中恢复由4帧图像组成的5×5视点动态光场。相比当前较先进的方法,所提方法具有更强的鲁棒性,恢复的动态光场图像帧保留了更准确的细节。通过反复捕获和恢复这些压缩测量值,重建图像的时间帧率是原始相机帧率的4倍。

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.