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In the regime of wavefront shaping (WFS) techniques, both the transmission matrix (TM) methods and a recently proposed third-order correlation of light fields (TCLF) method are effective in overcoming light scattering. This letter details the relationship between the amplitude-only TM method and the amplitude-only TCLF. The random fluctuations of different pixels on a digital micromirror device (DMD) in the amplitude-only TCLF method can be regarded as orthogonal bases, sharing the same role as the Hadamard bases in the amplitude-only TM method. This insight explains why the computational complexity of the TCLF method is significantly lower than that of the TM methods and also indicates that the amplitude-only TM is essentially a special case of the TCLF method.

Human pose estimation has seen widespread use of transformer models in recent years. Pose transformers benefit from the self-attention map, which captures the correlation between human joint tokens and the image. However, training such models is computationally expensive. The recent token-Pruned Pose Transformer (PPT) solves this problem by pruning the background tokens of the image, which are usually less informative. However, although it improves efficiency, PPT inevitably leads to worse performance than TokenPose due to the pruning of tokens. To overcome this problem, we present a novel method called Distilling Pruned-Token Transformer for human pose estimation (DPPT). Our method leverages the output of a pre-trained TokenPose to supervise the learning process of PPT. We also establish connections between the internal structure of pose transformers and PPT, such as attention maps and joint features. Our experimental results on the MPII datasets show that our DPPT can significantly improve PCK compared to previous PPT models while still reducing computational complexity.