In recent years， anchor-based 3D hand pose estimation methods are becoming popular， and Anchor-to-Joint （A2J） is one of the more representative methods. In A2J， anchor points are densely set on depth map， and neural network is used to predict offsets between anchor points and key points together with weights of anchor points； predicted offsets and weights are used to calculate the coordinates of key points in a weighted summation mode to reduce noise in network regression results. A2J methods are simple and effective， but they are sensitive to ill-suited network structure and prone to inaccurate regression due to loss function. Therefore， an improved network HigherA2J was proposed. Firstly， a single branch jointly predicted X， Y and Z offsets between anchors and key points to better utilize 3D characteristics of depth map； secondly， network branch structure was simplified to reduce network parameters； finally， the loss function for key point estimation was designed， combined with offset estimation loss， which improved the overall estimation accuracy effectively. Experimental results show the reductions in average hand pose estimation error of 0.32 mm， 0.35 mm and 0.10 mm compared to conventional A2J on three datasets NYU， ICVL and HANDS 2017 respectively.

Compressed sensing mainly contains random projection and reconstruction. Because of lower convergence speed of iterative shrinkage algorithm and the lacking of direction of traditional 2-dimensional wavelet transform， random projection was implemented by using Permute Discrete Cosine Transform （PDCT）， and the gradient projection was used for reconstruction. Based on the simplification of computation complexity， the transformation coefficients in the dual-tree complex wavelet domain were improved by iteration. Finally， the reconstructed image was obtained by the inverse transform. In the experiments， the reconstruction results of DT CWT （Dual-Tree Complex Wavelet Transform） and bi-orthogonal wavelet were compared with the same reconstruction algorithm， and the former is better than the latter in image detail and smoothness with higher Peak Signal-to-Noise Ratio （PSNR） of 1.5 dB. In the same sparse domain， gradient projection converges faster than iterative shrinkage algorithm. And in the same sparse domain and random projection， PDCT has a slightly higher PSNR than the structural random matrix.